{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "315023b5-9e9b-44df-8d69-be506105fbe5",
   "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",
      "Assimulo CVode available\n",
      "RateChar is available\n",
      "Parallel scanner is available\n",
      "\n",
      "PySCeS environment\n",
      "******************\n",
      "pysces.model_dir = C:\\Users\\27629\\Pysces\\psc\n",
      "pysces.output_dir = C:\\Users\\27629\\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",
    "import scipy as sp\n",
    "import pandas as pd \n",
    "import scipy.optimize\n",
    "import pysces\n",
    "from matplotlib import pyplot as plt\n",
    "import os\n",
    "import copy\n",
    "from lmfit import Model\n",
    "backupdir = os.getcwd() \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e347d37c-1d07-4bad-8939-9a75183b7fbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('A0.5B24.csv', names=['time','NADH'])\n",
    "df2 = pd.read_csv('A1B24.csv', names=['time','NADH'])\n",
    "df3 = pd.read_csv('A2B24.csv', names=['time', 'NADH'])\n",
    "df4 = pd.read_csv('A4B24.csv', names=['time','NADH'])\n",
    "df5 = pd.read_csv('A8B24.csv', names=['time','NADH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e8473330-dc70-4c0b-a8dc-317971c69096",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x230e69b4f50>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "efc264ae214e4ad9823ebb5126b3c4dc",
       "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(df1.time,df1.NADH, label='A0.5')\n",
    "ax.scatter(df2.time,df2.NADH, label='A1')\n",
    "ax.scatter(df3.time,df2.NADH, label='A2')\n",
    "ax.scatter(df4.time,df4.NADH, label='A4')\n",
    "ax.scatter(df5.time,df5.NADH, label='A8')\n",
    "ax.set_xlabel('Time (s)')\n",
    "ax.set_ylabel('[NADH] (mM)')\n",
    "ax.legend(loc='best')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9fb4ba71-8ce3-4a74-b8f2-bfd01801e5d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df6 = pd.read_csv('A8B1.5.csv',names=['time','NADH'])\n",
    "df7 = pd.read_csv('A8B3.csv',names=['time','NADH'])\n",
    "df8 = pd.read_csv('A8B6.csv',names=['time','NADH'])\n",
    "df9 = pd.read_csv('A8B12.csv',names=['time','NADH'])\n",
    "df10 = pd.read_csv('A8B24.csv',names=['time','NADH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "44dda968-3764-49f5-9687-a79008edde4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x230e6c90cd0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "743045c0590843f0a3604388e8bc2b1d",
       "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(df6.time, df6.NADH, label='B1.5')\n",
    "ax.scatter(df7.time, df7.NADH, label='B3')\n",
    "ax.scatter(df8.time, df8.NADH, label='B6')\n",
    "ax.scatter(df9.time, df9.NADH, label='B12')\n",
    "ax.scatter(df10.time,df10.NADH, label='B24')\n",
    "ax.set_xlabel('Time (s)')\n",
    "ax.set_ylabel('[NADH] (mM)')\n",
    "ax.legend(loc='best')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "da65adac-fbdc-4b1e-8889-f007710e62d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "regA0_5 = sp.stats.linregress(df1.time,df1.NADH)\n",
    "regA1 = sp.stats.linregress(df2.time,df2.NADH)\n",
    "regA2 = sp.stats.linregress(df3.time,df3.NADH)\n",
    "regA4 = sp.stats.linregress(df4.time,df4.NADH)\n",
    "regA8 = sp.stats.linregress(df5.time,df5.NADH)\n",
    "regB1_5 = sp.stats.linregress(df6.time,df6.NADH)\n",
    "regB3 = sp.stats.linregress(df7.time,df7.NADH)\n",
    "regB6 = sp.stats.linregress(df8.time,df8.NADH)\n",
    "regB12 = sp.stats.linregress(df9.time,df9.NADH)\n",
    "regB24 = sp.stats.linregress(df10.time,df10.NADH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0396008d-e0d3-489c-ae57-0286bd8ea9bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.24118661159479945\n",
      "0.3526304449083254\n",
      "0.5746500696105542\n",
      "0.7322856639928838\n",
      "0.8250788434733142\n",
      "0.25422083243133853\n",
      "0.3679535702854622\n",
      "0.5564088293747242\n",
      "0.701203980466376\n",
      "0.8250788434733142\n"
     ]
    }
   ],
   "source": [
    "regressions = [regA0_5, regA1, regA2, regA4, regA8, regB1_5,regB3, regB6, regB12, regB24]\n",
    "rates = []\n",
    "for reg in regressions:\n",
    "    print(reg.slope)\n",
    "    rates.append(reg.slope)\n",
    "rates = np.array(rates)\n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "aaf35b39-0243-47d4-8993-2797ab61d0c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.24118661, 0.35263044, 0.57465007, 0.73228566, 0.82507884,\n",
       "       0.25422083, 0.36795357, 0.55640883, 0.70120398, 0.82507884])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "355c7858-a00d-4e7c-9d5e-754eeaeae612",
   "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>0.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.241187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.352630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.574650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.732286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.825079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.254221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.367954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.556409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.701204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</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  0.5  24.0  0.241187\n",
       "1  1.0  24.0  0.352630\n",
       "2  2.0  24.0  0.574650\n",
       "3  4.0  24.0  0.732286\n",
       "4  8.0  24.0  0.825079\n",
       "5  8.0   1.5  0.254221\n",
       "6  8.0   3.0  0.367954\n",
       "7  8.0   6.0  0.556409\n",
       "8  8.0  12.0  0.701204\n",
       "9  8.0  24.0  0.825079"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concs = np.array([0.5, 1, 2, 4, 8, 8, 8, 8, 8, 8])\n",
    "conc = np.array([24, 24, 24, 24, 24, 1.5, 3, 6, 12, 24])\n",
    "s_and_v = pd.DataFrame({'a': concs,'b': conc, 'rate': rates})\n",
    "s_and_v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6968bade-7a0c-4fab-a052-4e90cfeb6122",
   "metadata": {},
   "outputs": [],
   "source": [
    "def v(Vf, Ka, Kb, a, b):\n",
    "    return (Vf*a*b)/((Ka+a)*(Kb+b))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c7d5aed6-0cb5-4759-882d-5a86f2e51608",
   "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[44]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m mymod = Model(v)\n\u001b[32m      2\u001b[39m mypar = mymod.make_params(Vf=\u001b[32m1\u001b[39m,Ka=\u001b[32m1\u001b[39m,Kb=\u001b[32m1\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m myfit = \u001b[43mmymod\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrates\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmypar\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43ma\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconcs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mb\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m      4\u001b[39m myfit\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~\\miniconda3\\envs\\minicourse\\Lib\\site-packages\\lmfit\\model.py:1121\u001b[39m, in \u001b[36mModel.fit\u001b[39m\u001b[34m(self, data, params, weights, method, iter_cb, scale_covar, verbose, fit_kws, nan_policy, calc_covar, max_nfev, coerce_farray, **kwargs)\u001b[39m\n\u001b[32m   1119\u001b[39m         params[name] = deepcopy(p)\n\u001b[32m   1120\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1121\u001b[39m         \u001b[43mparams\u001b[49m\u001b[43m[\u001b[49m\u001b[43mname\u001b[49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[43mset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m=\u001b[49m\u001b[43mp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1122\u001b[39m     \u001b[38;5;28;01mdel\u001b[39;00m kwargs[name]\n\u001b[32m   1124\u001b[39m \u001b[38;5;66;03m# All remaining kwargs should correspond to independent variables.\u001b[39;00m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~\\miniconda3\\envs\\minicourse\\Lib\\site-packages\\lmfit\\parameter.py:863\u001b[39m, in \u001b[36mParameter.set\u001b[39m\u001b[34m(self, value, vary, min, max, expr, brute_step, is_init_value)\u001b[39m\n\u001b[32m    861\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m value \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    862\u001b[39m     is_init_value = is_init_value \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m.value \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;28;01mNone\u001b[39;00m, -inf, inf)\n\u001b[32m--> \u001b[39m\u001b[32m863\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mvalue\u001b[49m = value\n\u001b[32m    864\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m is_init_value:\n\u001b[32m    865\u001b[39m         \u001b[38;5;28mself\u001b[39m.init_value = value\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~\\miniconda3\\envs\\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": [
    "mymod = Model(v)\n",
    "mypar = mymod.make_params(Vf=1,Ka=1,Kb=1)\n",
    "myfit = mymod.fit(rates, mypar, a=concs, b=conc)\n",
    "myfit\n",
    "\n"
   ]
  }
 ],
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