{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3f0089b6-4628-41b6-a538-92df3e355f11",
   "metadata": {},
   "source": [
    "# Tutorial Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2584a855-4dce-479f-8735-cb3bd30d9d52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matplotlib backend set to: \"module://ipympl.backend_nbagg\"\n",
      "Matplotlib interface loaded (pysces.plt.m)\n",
      "Continuation routines available\n",
      "NLEQ2 routines available\n",
      "SBML support available\n",
      "You are using NumPy (2.4.2) with SciPy (1.17.0)\n",
      "RateChar is available\n",
      "Parallel scanner is available\n",
      "\n",
      "PySCeS environment\n",
      "******************\n",
      "pysces.model_dir = C:\\Users\\DELL\\Pysces\\psc\n",
      "pysces.output_dir = C:\\Users\\DELL\\Pysces\n",
      "\n",
      "\n",
      "***********************************************************************\n",
      "* Welcome to PySCeS (1.2.3) - Python Simulator for Cellular Systems   *\n",
      "*                http://pysces.sourceforge.net                        *\n",
      "* Copyright(C) B.G. Olivier, J.M. Rohwer, J.-H.S. Hofmeyr, 2004-2025  *\n",
      "* Triple-J Group for Molecular Cell Physiology                        *\n",
      "* Stellenbosch University, ZA and VU University Amsterdam, NL         *\n",
      "* PySCeS is distributed under the PySCeS (BSD style) licence, see     *\n",
      "* LICENCE.txt (supplied with this release) for details                *\n",
      "* Please cite PySCeS with: doi:10.1093/bioinformatics/bti046          *\n",
      "***********************************************************************\n"
     ]
    }
   ],
   "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 pysces\n",
    "from lmfit import Model\n",
    "backupdir = os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff03b505-b587-4569-a979-c1359aaf85c8",
   "metadata": {},
   "source": [
    "## Question 1\n",
    "Visualization of raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3947c19c-006e-4e6c-8f4a-1758f2276912",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import CSV files for varied a\n",
    "avar0 = pd.read_csv('A0.5B24.csv', names=['Time', 'NADPH'])\n",
    "avar1 = pd.read_csv('A1B24.csv', names=['Time', 'NADPH'])\n",
    "avar2 = pd.read_csv('A2B24.csv', names=['Time', 'NADPH'])\n",
    "avar4 = pd.read_csv('A4B24.csv', names=['Time', 'NADPH'])\n",
    "avar8 = pd.read_csv('A8B24.csv', names=['Time', 'NADPH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f469324a-5a85-4bef-8396-40f788164903",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f348791be0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4ec584fa81324ce486612dda452d7b66",
       "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": [
    "#plotting change in a\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(avar0.Time, avar0.NADPH, 'o', label='a= 0.5 mM')\n",
    "ax.plot(avar1.Time, avar1.NADPH, 'o', label='a= 1 mM')\n",
    "ax.plot(avar2.Time, avar2.NADPH, 'o', label='a= 2 mM')\n",
    "ax.plot(avar4.Time, avar4.NADPH, 'o', label='a= 4 mM')\n",
    "ax.plot(avar8.Time, avar8.NADPH, 'o', label='a= 8 mM')\n",
    "ax.set_xlabel('Time (min)')\n",
    "ax.set_ylabel('nadph (mM)')\n",
    "ax.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d73ae682-92b2-45dc-9178-22bae8867f72",
   "metadata": {},
   "outputs": [],
   "source": [
    "bvar0 = pd.read_csv('A0B0.csv', names=['Time', 'NADPH'])\n",
    "bvar1 = pd.read_csv('A8B1.5.csv', names=['Time', 'NADPH'])\n",
    "bvar3 = pd.read_csv('A8B3.csv', names=['Time', 'NADPH'])\n",
    "bvar6 = pd.read_csv('A8B6.csv', names=['Time', 'NADPH'])\n",
    "bvar2 = pd.read_csv('A8B12.csv', names=['Time', 'NADPH'])\n",
    "bvar4 = pd.read_csv('A8B24.csv', names=['Time', 'NADPH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0c5e840f-b321-42ea-9623-949a66cb00fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f349933ed0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8b692180b3c74a3f9c936b174f1c09eb",
       "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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      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting change in b\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(bvar1.Time, bvar1.NADPH, 'o', label='b= 1.5 mM')\n",
    "ax.plot(bvar3.Time, bvar3.NADPH, 'o', label='b= 3 mM')\n",
    "ax.plot(bvar6.Time, bvar6.NADPH, 'o', label='b= 6 mM')\n",
    "ax.plot(bvar2.Time, bvar2.NADPH, 'o', label='b= 12 mM')\n",
    "ax.plot(bvar4.Time, bvar4.NADPH, 'o', label='b= 24 mM')\n",
    "ax.set_xlabel('Time (min)')\n",
    "ax.set_ylabel('nadph (mM)')\n",
    "ax.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e644742-def9-4249-97e2-d1230bc5bff0",
   "metadata": {},
   "source": [
    "## Question 2\n",
    "Calculating initial rates "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "91cababd-3170-4f07-b5e3-f2561eaf0304",
   "metadata": {},
   "outputs": [],
   "source": [
    "#linear regression\n",
    "rA0B0 = sp.stats.linregress(bvar0.Time, bvar0.NADPH)\n",
    "rA05B24 = sp.stats.linregress(avar0.Time, avar0.NADPH)\n",
    "rA1B24 = sp.stats.linregress(avar1.Time, avar1.NADPH)\n",
    "rA2B24 = sp.stats.linregress(avar2.Time, avar2.NADPH)\n",
    "rA4B24 = sp.stats.linregress(avar4.Time, avar4.NADPH)\n",
    "rA8B24 = sp.stats.linregress(avar8.Time, avar8.NADPH)\n",
    "rA8B15 = sp.stats.linregress(bvar1.Time, bvar1.NADPH)\n",
    "rA8B3 = sp.stats.linregress(bvar3.Time, bvar3.NADPH)\n",
    "rA8B6 = sp.stats.linregress(bvar6.Time, bvar6.NADPH)\n",
    "rA8B12 = sp.stats.linregress(bvar2.Time, bvar2.NADPH)\n",
    "# rA8B24 = sp.stats.linregress(bvar4.Time, bvar4.NADPH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "32c4ab3b-c1cd-42a8-9c3c-aeaa4ea35047",
   "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": [
    "#combining data\n",
    "regressions = [rA0B0, rA05B24, rA1B24, rA2B24, rA4B24, rA8B24, rA8B15, rA8B3, rA8B6, rA8B12]\n",
    "rates = []\n",
    "for reg in regressions:\n",
    "    print(reg.slope)\n",
    "    rates.append(reg.slope)\n",
    "rates = np.array(rates)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecfaba28-8cb7-4f6a-8b41-d636f509ebb1",
   "metadata": {},
   "source": [
    "## Question 3\n",
    "Producing a new dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f824961d-5aee-4c36-bf22-985dc6d2a7c4",
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.array([0, 0.5, 1, 2, 4, 8, 8, 8, 8, 8])\n",
    "B = np.array([0, 24, 24, 24, 24, 24, 1.5, 3, 6, 12])\n",
    "data = pd.DataFrame({'a': A, 'b': B, 'rates': rates})\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e129f8b-f531-4adf-8417-b4629a979927",
   "metadata": {},
   "source": [
    "## Question 4\n",
    "Fitting to model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "97714b2b-e473-4c19-abaa-8f7f2ece3288",
   "metadata": {},
   "outputs": [],
   "source": [
    "def v(Vf, Ka, Kb, a, b):\n",
    "    return (Vf*a*b)/((Ka+a)*(Kb+b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ae4a72af-2ae4-443d-93f8-ee6b0d01bbd5",
   "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'>5</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'>4</td></tr><tr><td style='text-align:left'>chi-square</td><td style='text-align:right'> 2.73105972</td></tr><tr><td style='text-align:left'>reduced chi-square</td><td style='text-align:right'> 0.45517662</td></tr><tr><td style='text-align:left'>Akaike info crit.</td><td style='text-align:right'>-4.97895382</td></tr><tr><td style='text-align:left'>Bayesian info crit.</td><td style='text-align:right'>-3.76861345</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'>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'>a</td><td style='text-align:left'> 0.00000000</td><td style='text-align:left'>0.0</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'>b</td><td style='text-align:left'> 0.00000000</td><td style='text-align:left'>0.0</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": [
       "<lmfit.model.ModelResult at 0x1f349d10c20>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mymod = Model(v)\n",
    "#mypar = mymod.make_params(Vf=1, Ka=1, Kb=1)\n",
    "myfit = mymod.fit(rates, Vf=1, Ka=1, Kb=1, a=A.all(), b=B.all())\n",
    "myfit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c962b851-27b3-42e3-839d-1d10f22caf0f",
   "metadata": {},
   "source": [
    "## Question 5\n",
    "Plotting the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f4dc9646-b12d-49fc-956b-dd60a5cd39a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f34fa656d0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e62a4b331bc94d9394cfa662ddc13a39",
       "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": [
    "#rates vs a \n",
    "avals = np.linspace(0,10,101)\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(data.a, data.rates, 'o', label='data')\n",
    "ax.plot(avals, v(1, 1, 1, avals, 10), label='fit')\n",
    "ax.set_xlabel('a (mM)')\n",
    "ax.set_ylabel('rate (mM/min)')\n",
    "ax.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3cfe1fb1-c42c-4adf-b3cc-a5cb550ae037",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[32]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m      3\u001b[39m fig, ax = plt.subplots()\n\u001b[32m      4\u001b[39m ax.plot(data.b, data.rates, \u001b[33m'\u001b[39m\u001b[33mo\u001b[39m\u001b[33m'\u001b[39m, label=\u001b[33m'\u001b[39m\u001b[33mdata\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m ax.plot(bvals, \u001b[43mmyfit\u001b[49m\u001b[43m.\u001b[49m\u001b[43meval\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbvals\u001b[49m\u001b[43m)\u001b[49m, label=\u001b[33m'\u001b[39m\u001b[33mfit\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m      6\u001b[39m ax.set_xlabel(\u001b[33m'\u001b[39m\u001b[33ma (mM)\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m      7\u001b[39m ax.set_ylabel(\u001b[33m'\u001b[39m\u001b[33mrate (mM/min)\u001b[39m\u001b[33m'\u001b[39m)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~\\minicourse\\Lib\\site-packages\\lmfit\\model.py:1616\u001b[39m, in \u001b[36mModelResult.eval\u001b[39m\u001b[34m(self, params, **kwargs)\u001b[39m\n\u001b[32m   1614\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m params \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m   1615\u001b[39m     params = \u001b[38;5;28mself\u001b[39m.params\n\u001b[32m-> \u001b[39m\u001b[32m1616\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m.\u001b[49m\u001b[43meval\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m=\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43muserkws\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~\\minicourse\\Lib\\site-packages\\lmfit\\model.py:1004\u001b[39m, in \u001b[36mModel.eval\u001b[39m\u001b[34m(self, params, **kwargs)\u001b[39m\n\u001b[32m    971\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34meval\u001b[39m(\u001b[38;5;28mself\u001b[39m, params=\u001b[38;5;28;01mNone\u001b[39;00m, **kwargs):\n\u001b[32m    972\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"Evaluate the model with supplied parameters and keyword arguments.\u001b[39;00m\n\u001b[32m    973\u001b[39m \n\u001b[32m    974\u001b[39m \u001b[33;03m    Parameters\u001b[39;00m\n\u001b[32m   (...)\u001b[39m\u001b[32m   1002\u001b[39m \n\u001b[32m   1003\u001b[39m \u001b[33;03m    \"\"\"\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1004\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m coerce_arraylike(\u001b[38;5;28mself\u001b[39m.func(**\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmake_funcargs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m))\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~\\minicourse\\Lib\\site-packages\\lmfit\\model.py:923\u001b[39m, in \u001b[36mModel.make_funcargs\u001b[39m\u001b[34m(self, params, kwargs, strip)\u001b[39m\n\u001b[32m    921\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m params:\n\u001b[32m    922\u001b[39m         saved_values[name] = params[name].value\n\u001b[32m--> \u001b[39m\u001b[32m923\u001b[39m         \u001b[43mparams\u001b[49m\u001b[43m[\u001b[49m\u001b[43mname\u001b[49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[43mvalue\u001b[49m = val\n\u001b[32m    925\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(saved_values) > \u001b[32m0\u001b[39m:\n\u001b[32m    926\u001b[39m     params.update_constraints()\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~\\minicourse\\Lib\\site-packages\\lmfit\\parameter.py:1020\u001b[39m, in \u001b[36mParameter.value\u001b[39m\u001b[34m(self, val)\u001b[39m\n\u001b[32m   1018\u001b[39m \u001b[38;5;28mself\u001b[39m._val = val\n\u001b[32m   1019\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._val \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1020\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_val\u001b[49m\u001b[43m \u001b[49m\u001b[43m>\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmax\u001b[49m:\n\u001b[32m   1021\u001b[39m         \u001b[38;5;28mself\u001b[39m._val = \u001b[38;5;28mself\u001b[39m.max\n\u001b[32m   1022\u001b[39m     \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._val < \u001b[38;5;28mself\u001b[39m.min:\n",
      "\u001b[31mValueError\u001b[39m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
     ]
    }
   ],
   "source": [
    "#rates vs b\n",
    "\n",
    "bvals = np.linspace(0,25,101)\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(data.b, data.rates, 'o', label='data')\n",
    "ax.plot(bvals, v(1, 1, 1, 10, bvals), label='fit')\n",
    "ax.set_xlabel('b (mM)')\n",
    "ax.set_ylabel('rate (mM/min)')\n",
    "ax.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8b3841a-133e-4962-82d2-10df9af31ebb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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