{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d426a80b-4420-4e0d-a907-c5ad8d5f75d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "from matplotlib import pyplot as plt\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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39b4b0b8-9488-436f-8976-5686d5c56ba1",
   "metadata": {},
   "outputs": [],
   "source": [
    "Question 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "673e3b23-bc28-4c55-9041-935e172287ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "m1 = pd.read_csv('A0.5B24.csv', sep=',', names = ['Time','NADPH'])\n",
    "m2 = pd.read_csv('A1B24.csv', sep=',', names = ['Time','NADPH'])\n",
    "m3 = pd.read_csv('A2B24.csv', sep=',', names = ['Time','NADPH'])\n",
    "m4 = pd.read_csv('A4B24.csv', sep=',', names = ['Time','NADPH'])\n",
    "m5 = pd.read_csv('A8B24.csv', sep=',', names = ['Time','NADPH'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "779fba10-64e1-4350-b307-d4eaba124a38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Time', ylabel='NADPH'>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m1.plot(x='Time', y='NADPH', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "740802e9-426b-4724-82bc-7cf7eb03d55c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Time', ylabel='NADPH'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m2.plot(x='Time', y='NADPH', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4f28b2cb-adf0-4eb4-bae8-58e9c05d846a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Time', ylabel='NADPH'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m3.plot(x='Time', y='NADPH', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "18294e61-7d9f-4e2f-b436-3cf10b1db493",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Time', ylabel='NADPH'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m4.plot(x='Time', y='NADPH', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "47bb7cc5-2218-4768-b7fe-c0290d27bf5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Time', ylabel='NADPH'>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m5.plot(x='Time', y='NADPH', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "03a51440-b41d-4eba-b049-ca88de0f490b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "mm1 = pd.read_csv('A8B1.5.csv', sep=',', names = ['Time','NADPH'])\n",
    "mm2 = pd.read_csv('A8B3.csv', sep=',', names = ['Time','NADPH'])\n",
    "mm3 = pd.read_csv('A8B6.csv', sep=',', names = ['Time','NADPH'])\n",
    "mm4 = pd.read_csv('A8B12.csv', sep=',', names = ['Time','NADPH'])\n",
    "mm5 = pd.read_csv('A8B24.csv', sep=',', names = ['Time','NADPH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1c70474c-8ff4-4227-b5cb-d6e66a73036f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Time', ylabel='NADPH'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mm1.plot(x='Time', y='NADPH', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c84baccb-97a5-46fd-92b0-84388a06fead",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Time', ylabel='NADPH'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mm2.plot(x='Time', y='NADPH', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e45fa650-c2bb-4c05-a3e5-95f82d7edf39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Time', ylabel='NADPH'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mm3.plot(x='Time', y='NADPH', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "99ec3024-2777-4ffb-8872-fc8caac12cb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Time', ylabel='NADPH'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mm4.plot(x='Time', y='NADPH', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ce8de165-dbfc-484c-ab39-b2c6e107c9ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Time', ylabel='NADPH'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mm5.plot(x='Time', y='NADPH', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "893c5796-981a-471b-a6a1-183e41a31ff9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy as sp\n",
    "reg1 = sp.stats.linregress(m1.Time, m1.NADPH)\n",
    "reg2 = sp.stats.linregress(m2.Time, m2.NADPH)\n",
    "reg3 = sp.stats.linregress(m3.Time, m3.NADPH)\n",
    "reg4 = sp.stats.linregress(m4.Time, m4.NADPH)\n",
    "reg5 = sp.stats.linregress(m5.Time, m5.NADPH)\n",
    "\n",
    "reg1_1 = sp.stats.linregress(mm1.Time, mm1.NADPH)\n",
    "reg2_2 = sp.stats.linregress(mm2.Time, mm2.NADPH)\n",
    "reg3_3 = sp.stats.linregress(mm3.Time, mm3.NADPH)\n",
    "reg4_4 = sp.stats.linregress(mm4.Time, mm4.NADPH)\n",
    "reg5_5 = sp.stats.linregress(mm4.Time, mm4.NADPH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "59b16124-0c6c-4edb-b938-167b156efb86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.24118661159479945\n",
      "0.3526304449083254\n",
      "0.5746500696105541\n",
      "0.7322856639928835\n",
      "0.8250788434733143\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "regressions_A = [reg1, reg2, reg3, reg4, reg5]\n",
    "rates = []\n",
    "for reg in regressions_A:\n",
    "    print(reg.slope)\n",
    "    rates.append(reg.slope)\n",
    "rates_A = np.array(rates)\n",
    "\n",
    "# obtain its slope (i.e. the rate of the reaction)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "cf0630ee-01cb-4a60-b528-3bd4e2e3743b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.24118661, 0.35263044, 0.57465007, 0.73228566, 0.82507884])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rates_A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "0b8bbca7-044e-4409-91ac-bc884acb018f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.25422083243133853\n",
      "0.3679535702854621\n",
      "0.556408829374724\n",
      "0.7012039804663761\n",
      "0.7012039804663761\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "regressions_B = [reg1_1, reg2_2, reg3_3, reg4_4, reg5_5]\n",
    "rates = []\n",
    "for reg in regressions_B:\n",
    "    print(reg.slope)\n",
    "    rates.append(reg.slope)\n",
    "rates_B = np.array(rates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "90bff0a6-7914-4b6b-ba0c-b4fb3b195b89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.25422083, 0.36795357, 0.55640883, 0.70120398, 0.70120398])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rates_B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "6c9111d4-e589-4262-a3a1-a0c1681625f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "concs1 = np.array([1, 2, 3, 4, 5])\n",
    "a_rate = pd.DataFrame({'A variants': concs, 'Rate': rates})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "e174ee79-0d3f-44be-bc00-2804cbd683be",
   "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 variants</th>\n",
       "      <th>Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>0.254221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>0.367954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>0.556409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>0.701204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>0.701204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A variants      Rate\n",
       "0          11  0.254221\n",
       "1          22  0.367954\n",
       "2          33  0.556409\n",
       "3          44  0.701204\n",
       "4          55  0.701204"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "8170b72a-f46e-4fee-9127-af4c1b1d17b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "concs2 = np.array([1_1, 2_2, 3_3, 4_4, 5_5])\n",
    "b_rate = pd.DataFrame({'B variants': concs, 'Rate': rates})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "8565aeac-b761-4d36-a3b9-38a2475be432",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>B variants</th>\n",
       "      <th>Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "   B variants      Rate\n",
       "0          11  0.254221\n",
       "1          22  0.367954\n",
       "2          33  0.556409\n",
       "3          44  0.701204\n",
       "4          55  0.701204"
      ]
     },
     "execution_count": 114,
     "metadata": {},
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    }
   ],
   "source": [
    "b_rate\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "c4248059-66df-48cf-8792-952f6f615c50",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "combo = pd.concat([a_rate, b_rate, ], keys=[\"a\", \"b\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "233dc1d7-4941-4214-9e82-f46af3212336",
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    {
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      "text/plain": [
       "     A variants      Rate  B variants\n",
       "a 0        11.0  0.254221         NaN\n",
       "  1        22.0  0.367954         NaN\n",
       "  2        33.0  0.556409         NaN\n",
       "  3        44.0  0.701204         NaN\n",
       "  4        55.0  0.701204         NaN\n",
       "b 0         NaN  0.254221        11.0\n",
       "  1         NaN  0.367954        22.0\n",
       "  2         NaN  0.556409        33.0\n",
       "  3         NaN  0.701204        44.0\n",
       "  4         NaN  0.701204        55.0"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "55596e70-1033-42c8-b31c-9384d45c3c45",
   "metadata": {},
   "outputs": [],
   "source": [
    "from lmfit import Model\n",
    "def v (Vf, a, b, Ka):\n",
    "    return Vf*ab / (Ka + a)(Kb + b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "c22a755a-2ac0-496b-afac-87b183e9a9c3",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "'Missing independent variable 'Vf'",
     "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[167]\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[32m0\u001b[39m,Ka=\u001b[32m0\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[43ms\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconcs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m      4\u001b[39m myfit\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~\\OneDrive - Stellenbosch University\\Documents\\CODING\\envs\\minicourse\\Lib\\site-packages\\lmfit\\model.py:1162\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   1160\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m var \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m params \u001b[38;5;129;01mand\u001b[39;00m var \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.opts:\n\u001b[32m   1161\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m var \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m kwargs:\n\u001b[32m-> \u001b[39m\u001b[32m1162\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[33m'\u001b[39m\u001b[33mMissing independent variable \u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvar\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m   1163\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np.isscalar(kwargs[var]):\n\u001b[32m   1164\u001b[39m         kwargs[var] = _align(kwargs[var], mask, data)\n",
      "\u001b[31mValueError\u001b[39m: 'Missing independent variable 'Vf'"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "mymod = Model(v)\n",
    "mypar = mymod.make_params(Vf=0,Ka=0)\n",
    "myfit = mymod.fit(rates, mypar, s=concs)\n",
    "myfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b69eedc3-fb11-4663-9a1a-db9f94458343",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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