{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6160f875-767e-4da5-a961-6843e5113c6b",
   "metadata": {},
   "source": [
    "ASSIGNMENT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8b7503cd-fe06-4263-a576-c75cae9c02c7",
   "metadata": {},
   "outputs": [],
   "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",
    "from numdifftools import Derivative\n",
    "backupdir = os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3bf063e-2608-4487-8825-011f17278117",
   "metadata": {},
   "source": [
    "QUESTION 1 :UPLOAD OF CSV FILES AND DATA VISUALIZATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "b6725344-515e-437e-8bbb-1d4590dee063",
   "metadata": {},
   "outputs": [],
   "source": [
    "A0_5B24 =  pd.read_csv ('A0.5B24.csv', sep=',', names = ['Time', 'NADPH'])\n",
    "A0B0 = pd.read_csv ('A0B0.csv', sep= ',', names = ['Time', 'NADPH'])\n",
    "A1B24 = pd.read_csv('A1B24.csv', sep= ',',  names=['Time', 'NADPH'])\n",
    "A2B24 = pd.read_csv('A2B24.csv', sep= ',', names= ['Time', 'NADPH'] )\n",
    "A4B24 = pd.read_csv('A4B24.csv', sep= ',', names= ['Time', 'NADPH'] )\n",
    "A8B1_5 = pd.read_csv ('A8B1.5.csv', sep= ',', names = ['Time', 'NADPH'])\n",
    "A8B3 = pd.read_csv ('A8B3.csv', sep= ',', names = ['Time', 'NADPH'])\n",
    "A8B6 = pd.read_csv ('A8B6.csv', sep= ',', names = ['Time', 'NADPH'])\n",
    "A8B12 = pd.read_csv ('A8B12.csv', sep= ',', names = ['Time', 'NADPH'])\n",
    "A8B24 = pd.read_csv ('A8B24.csv', sep= ',', names = ['Time', 'NADPH'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "244a0483-f782-4006-a79e-73d22b0f6b25",
   "metadata": {},
   "source": [
    "GRAPH 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "9b3a96df-f995-4af6-9cb1-55eb80101805",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Time', ylabel='NADPH'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "04aec42dc5ed459c86ff52ac5ed61e5a",
       "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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' 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       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0149d5fe48db4f228d003cc82c8e7a0a",
       "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": [
    "A0_5B24.plot(x='Time', y='NADPH', kind='scatter')\n",
    "A1B24.plot(x='Time', y='NADPH', kind='scatter')\n",
    "A2B24.plot(x='Time', y='NADPH', kind='scatter')\n",
    "A4B24.plot(x='Time', y='NADPH', kind='scatter')\n",
    "A8B24.plot(x='Time', y='NADPH', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3dffa520-f9be-4f3c-b56b-2abcf15b1eae",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (101,) and (11, 2)",
     "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[54]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m sp = np.linspace(\u001b[32m0.01\u001b[39m, \u001b[32m5\u001b[39m, \u001b[32m101\u001b[39m)\n\u001b[32m      2\u001b[39m fig, ax =plt.subplots()\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[43max\u001b[49m\u001b[43m.\u001b[49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43msp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mA0_5B24\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mA0_5\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m      4\u001b[39m ax.plot(sp, A1B24, label= \u001b[33m'\u001b[39m\u001b[33mA1\u001b[39m\u001b[33m'\u001b[39m )\n\u001b[32m      5\u001b[39m ax.plot(sp, A2B24, label= \u001b[33m'\u001b[39m\u001b[33mA2\u001b[39m\u001b[33m'\u001b[39m)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~\\minicourse\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:1777\u001b[39m, in \u001b[36mAxes.plot\u001b[39m\u001b[34m(self, scalex, scaley, data, *args, **kwargs)\u001b[39m\n\u001b[32m   1534\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m   1535\u001b[39m \u001b[33;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[32m   1536\u001b[39m \n\u001b[32m   (...)\u001b[39m\u001b[32m   1774\u001b[39m \u001b[33;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[32m   1775\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m   1776\u001b[39m kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D)\n\u001b[32m-> \u001b[39m\u001b[32m1777\u001b[39m lines = [*\u001b[38;5;28mself\u001b[39m._get_lines(\u001b[38;5;28mself\u001b[39m, *args, data=data, **kwargs)]\n\u001b[32m   1778\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[32m   1779\u001b[39m     \u001b[38;5;28mself\u001b[39m.add_line(line)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~\\minicourse\\Lib\\site-packages\\matplotlib\\axes\\_base.py:297\u001b[39m, in \u001b[36m_process_plot_var_args.__call__\u001b[39m\u001b[34m(self, axes, data, return_kwargs, *args, **kwargs)\u001b[39m\n\u001b[32m    295\u001b[39m     this += args[\u001b[32m0\u001b[39m],\n\u001b[32m    296\u001b[39m     args = args[\u001b[32m1\u001b[39m:]\n\u001b[32m--> \u001b[39m\u001b[32m297\u001b[39m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    298\u001b[39m \u001b[43m    \u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[43m=\u001b[49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    299\u001b[39m \u001b[43m    \u001b[49m\u001b[43mreturn_kwargs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mreturn_kwargs\u001b[49m\n\u001b[32m    300\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~\\minicourse\\Lib\\site-packages\\matplotlib\\axes\\_base.py:494\u001b[39m, in \u001b[36m_process_plot_var_args._plot_args\u001b[39m\u001b[34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[39m\n\u001b[32m    491\u001b[39m     axes.yaxis.update_units(y)\n\u001b[32m    493\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m x.shape[\u001b[32m0\u001b[39m] != y.shape[\u001b[32m0\u001b[39m]:\n\u001b[32m--> \u001b[39m\u001b[32m494\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mx and y must have same first dimension, but \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    495\u001b[39m                      \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mhave shapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx.shape\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my.shape\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m    496\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m x.ndim > \u001b[32m2\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m y.ndim > \u001b[32m2\u001b[39m:\n\u001b[32m    497\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mx and y can be no greater than 2D, but have \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    498\u001b[39m                      \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mshapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx.shape\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my.shape\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n",
      "\u001b[31mValueError\u001b[39m: x and y must have same first dimension, but have shapes (101,) and (11, 2)"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "217fbc1f201f4d06a4811e53602c9c32",
       "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 src='data:image/png;base64,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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": [
    "sp = np.linspace(0.01, 5, 101)\n",
    "fig, ax =plt.subplots()\n",
    "ax.plot(sp, A0_5B24, label= 'A0_5')\n",
    "ax.plot(sp, A1B24, label= 'A1' )\n",
    "ax.plot(sp, A2B24, label= 'A2')\n",
    "ax.plot(sp, A4B24, label= 'A4')\n",
    "ax.plot(sp, A8B24, label= 'A8')\n",
    "ax.set_xlabel('Time(min)')\n",
    "ax.set_ylabel('NADPH (mM)')\n",
    "ax.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "169cb929-0082-4276-a20e-62b59080fb8d",
   "metadata": {},
   "source": [
    "GRAPH 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "2cb5eba1-0233-4599-8942-1e291da86fcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Time', ylabel='NADPH'>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A8B1_5.plot(x='Time', y='NADPH', kind='scatter')\n",
    "A8B3.plot(x='Time', y='NADPH', kind='scatter')\n",
    "A8B6.plot(x='Time', y='NADPH', kind='scatter')\n",
    "A8B12.plot(x='Time', y='NADPH', kind='scatter')\n",
    "A8B24.plot(x='Time', y='NADPH', kind='scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bbec5d9-0dbc-4ce9-95ac-c626b03686a4",
   "metadata": {},
   "source": [
    "QUESTION 2: REGRESSION CALCULATIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "d7f0475b-0c1d-49bc-b647-fead9add841b",
   "metadata": {},
   "outputs": [],
   "source": [
    "regA0_5B24 = sp.stats.linregress(A0_5B24.Time, A0_5B24.NADPH)\n",
    "regA0B0 = sp.stats.linregress(A0B0.Time, A0B0.NADPH)\n",
    "regA1B24 = sp.stats.linregress(A1B24.Time, A1B24.NADPH)\n",
    "regA2B24 = sp.stats.linregress(A2B24.Time, A2B24.NADPH)\n",
    "regA4B24 = sp.stats.linregress(A4B24.Time, A4B24.NADPH)\n",
    "regA8B1_5 = sp.stats.linregress(A8B1_5.Time, A8B1_5.NADPH)\n",
    "regA8B3 = sp.stats.linregress(A8B3.Time, A8B3.NADPH)\n",
    "regA8B6 = sp.stats.linregress(A8B6.Time, A8B6.NADPH)\n",
    "regA8B12 = sp.stats.linregress(A8B12.Time, A8B12.NADPH)\n",
    "regA8B24 = sp.stats.linregress(A8B24.Time, A8B24.NADPH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "8ad9c673-2995-48a8-a340-9277cf19c266",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.24118661159479945\n",
      "0.004954646341627726\n",
      "0.3526304449083254\n",
      "0.5746500696105541\n",
      "0.7322856639928835\n",
      "0.25422083243133853\n",
      "0.3679535702854621\n",
      "0.556408829374724\n",
      "0.7012039804663761\n",
      "0.8250788434733143\n"
     ]
    }
   ],
   "source": [
    "regressions = [regA0_5B24, regA0B0, regA1B24, regA2B24, regA4B24, regA8B1_5, regA8B3, regA8B6, regA8B12, regA8B24 ]\n",
    "rates = []\n",
    "for reg in regressions:\n",
    "    print (reg.slope)\n",
    "    rates.append(reg.slope)\n",
    "rates = np.array(rates) #RATE COMPUTATION COMPLETED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "fb9cf0dc-2c57-465c-9094-02f9cd8d6f02",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'LinregressResult' object has no attribute 'NADPH'",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mAttributeError\u001b[39m                            Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[67]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m concentrations = np.array([\u001b[43mregA0_5B24\u001b[49m\u001b[43m.\u001b[49m\u001b[43mNADPH\u001b[49m, regA0B0.NADPH, regA1B24.NADPH, regA2B24.NADPH, regA4B24.NADPH, regA8B1_5.NADPH, regA8B3.NADPH, regA8B6.NADPH, regA8B12.NADPH, regA8B24.NADPH])\n\u001b[32m      2\u001b[39m concentrations_and_rates = pd.DataFrame({\u001b[33m'\u001b[39m\u001b[33mNADPH\u001b[39m\u001b[33m'\u001b[39m:concentrations, \u001b[33m'\u001b[39m\u001b[33mRates\u001b[39m\u001b[33m'\u001b[39m:rates})\n",
      "\u001b[31mAttributeError\u001b[39m: 'LinregressResult' object has no attribute 'NADPH'"
     ]
    }
   ],
   "source": [
    "concentrations = np.array([regA0_5B24.NADPH, regA0B0.NADPH, regA1B24.NADPH, regA2B24.NADPH, regA4B24.NADPH, regA8B1_5.NADPH, regA8B3.NADPH, regA8B6.NADPH, regA8B12.NADPH, regA8B24.NADPH])\n",
    "concentrations_and_rates = pd.DataFrame({'NADPH':concentrations, 'Rates':rates})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "084da15c-297e-44dd-926e-a05e5eb67558",
   "metadata": {},
   "source": [
    "QUESTION 4: FITTING RATES TO THE EQUATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "987a15be-34bc-4f33-9e1d-d13a319cc07c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def M_M(Vf, a, b, Ka, Kb):\n",
    "    return Vf*a*b/ (Ka+a)*(Kb+b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1ad44ef-6484-425c-94af-b59e6a6b420a",
   "metadata": {},
   "outputs": [],
   "source": [
    "mymod = Model(M_M)\n",
    "mypar = mymod.make_params(Vf=1, Ka=1, Kb=1,)\n",
    "myfit = mymod.fit(rates, mypar, a=concentrations, b=concentrations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "63556b83-d55b-4f06-8cda-0daf8e7c7d01",
   "metadata": {},
   "outputs": [],
   "source": [
    "A = pd.Series([0, 0.5, 1, 2, 4, 8, 8, 8, 8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "30d6d011-e211-4c98-a84a-c94f6834b021",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "1    0.5\n",
       "2    1.0\n",
       "3    2.0\n",
       "4    4.0\n",
       "5    8.0\n",
       "6    8.0\n",
       "7    8.0\n",
       "8    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c15ee985-1b1c-4ddd-9a04-e0cd81d9932b",
   "metadata": {},
   "outputs": [],
   "source": [
    "B = pd.Series([0, 24, 24, 24, 24, 24, 1.5, 3, 6, 12])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3c97cf10-39bd-4392-a9c8-cd1cf91d0502",
   "metadata": {},
   "outputs": [],
   "source": [
    "rate = pd.Series([0, 0.512, 0.816, 1.201, 1.561, 1.747, 0.603, 0.912, 1.267, 1.558])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "23154edc-e983-4a8f-940c-833ef1040b8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.000\n",
       "1    0.512\n",
       "2    0.816\n",
       "3    1.201\n",
       "4    1.561\n",
       "5    1.747\n",
       "6    0.603\n",
       "7    0.912\n",
       "8    1.267\n",
       "9    1.558\n",
       "dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "885531c3-8e0a-4608-a6cf-7548955bf77b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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