{ "cells": [ { "cell_type": "code", "execution_count": 52, "id": "87897a46-d8e5-4011-9964-31c9bb6aefb7", "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", "from lmfit import Model\n", "from numdifftools import Derivative\n", "backupdir = os.getcwd()" ] }, { "cell_type": "code", "execution_count": 53, "id": "1db98198-7040-4bf5-a90c-922c66acf10d", "metadata": {}, "outputs": [], "source": [ "#A0B0 = pd.read_csv('A0B0.csv', names=['Time', 'NADPH'])\n", "A0_5 = pd.read_csv('A0.5B24.csv', names=['Time', 'NADPH'])\n", "A1 = pd.read_csv('A1B24.csv', names=['Time', 'NADPH'])\n", "A2 = pd.read_csv('A2B24.csv', names=['Time', 'NADPH'])\n", "A4 = pd.read_csv('A4B24.csv', names=['Time', 'NADPH'])\n", "A8 = pd.read_csv('A8B24.csv', names=['Time', 'NADPH'])" ] }, { "cell_type": "code", "execution_count": 54, "id": "86f8a3e8-8670-4094-a403-c45b39b11da4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3f057db0415440318baaf73b8273c93c", "version_major": 2, "version_minor": 0 }, "image/png": 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AAACcsBVcYmKijBw5sqwvCwAAgKPYCxghrVy50mRx9+vX74Se0MyZM6Vnz56mHJC+dK/hVatW8dQBAChHBIAOl5uXK6vTV8uC7xeYj3pcFnTbvuHDh8t//vMfk9l9vJYuXWpqQC5ZssQElQ0aNDC7wuzcudPS+wUAAA7cCxilt2jHIpmwaoJkZGcUtCXFJcnozqMlpWGKbY/0wIEDMnfuXFmzZo3Ztm/27NkyduzY43qv1157Lej4hRdekLffftvsDT148GCL7hgAAJQGI4AODv5GLh0ZFPypzOxM067n7TJv3jyzm4tu5ac7urz44ovi8/kKzletWrXE17Bhw4p97+zsbLNdYM2aNW27fwAAUE4jgMUlemhBaK0V2LRpU7n44osJBELQaV4d+fPJH0GXn7Z5xCMTV02UcxqcI9FR0bZM/2rgp3Tv5v3798tnn30mvXr1Mm26v3NJ4uPjiz03atQoqVu3rlkLCAAAIiwAXLt2raSlpZmizzqSpDZv3mwSC3R06f/+7//kzjvvlOXLl0urVq3suo2wlJaZVmTkr3AQmJ6dbvp1Su5k6bU3bdpkkjR09xYVExMjAwcONEGhPwDU4P14TJgwwewPresC9R8BAACE4svNlew1X8mRrCyJSUyUuI4dxMP2suERAPpH91566aWCESEdSbrhhhukR48ecuONN8rf/vY3ueOOO+Tjjz+26zbCUlZ2lqX9SkMDPd21RUfp/HT6NzY2VqZNmybVq1c307wl0dHDGTNmBLU9+eSTJgBctGiRnH766ZbfNwAgMngXLpSMRx+TI+npBW0xycmSNHaMxPfuXa73FklsCwCfeOIJs+1b4HSgBg8PPPCAyQK9/fbb5f777zefI1hiXKKl/Y6VBn5z5syRSZMmFfm+XHLJJWZ/Z13fV9op4Mcff9xsA6iBfseOHS29ZwBAZAV/O28foSMPQe1HMjLy26dOIQh0egCoo32ZmZlFpnd1JxCv12s+T0hIkMOHD9t1C2Grfe32JttXEz5CrQPUNYB6XvtZaf78+bJ3714ZOnSoCdYDXXbZZWZ0UAPA0kwBT5w40QT6r7/+ujRq1MhkFSt/wggAAP5pXx35Kxz85Z/0aRKBOV/tvPOYDnZyFrBOAV9//fVmLdn//vc/89LPNbjQ0SSla82aN29u1y2ELU3s0FIv/mAvkP94VOdRlieAaICnyRmFgz9/AKhlYdavX1+q93z22WdNkH/55ZdLnTp1Cl46JQwAgJ9Z8xcw7VuEz2fOaz84eATwueeeM+v7rrzySjO1aC4WEyNDhgyRp556yhxrMojWhUNRWudvcq/JIesAavBnRx3ADz74oNhznTt3DioFc6x++OGHE7wrAIAbaMKHlf1QTgGgTu/pNmAa7H3//fem7ZRTTgma9mvbtq1dl48IGuRpqRfN9tWED13zp9O+dpR+AQCgPGm2r5X9UM47gWjAR9bn8dNgz+pSLwAAOI2WetFsX034CLkO0OORmKQk0w8ODgAPHjxoyn7oll+aDJKXlxd03j8qCAAAoHX+tNSLyfb1eIKDQD3WZVBjx5AA4vQAUOv96e4R11xzjVn0rzuAAAAAFMfU+Zs6pWgdwKQk6gCGSwD40UcfyYcffijdu3e36xIAACACg0At9cJOIGEaANaoUYN9fgEAwHFNB1fp0pknF451AB9++GFTADg7O9uuSwAAAMBJI4C6ndi2bdskKSnJ7ABRoUKFoPNpaWl2XRoAAATssMF0KsosAPTv9gEAAMpvb90iCRXJySRUwL4AcNy4cTxeAADKMfgzJVUK1dTTOnumfeqU/KxbuJJtawAR3lauXCnR0dHSr18/y97zzTffNOWAGB0GEK7y8nyyc9Ne2bw63XzUY6dO++rIX8iCykfb9Lz2gztZOgJYs2ZN2bx5s9SqVctkAZdU+++XX36x8tIRq7zWbsyaNUuGDx9uPu7atUvq1q17Qu+newLfdddd0rNnT8vuEQDK0ra1mbJs7hY5uC+noK1KQqz0HNhMmrSr7ahvhvm9ETDtW4TPZ85rP7Jt3cnSAFD3/a1WrZr5fMqUKVa+tSuV19qNAwcOyNy5c2XNmjWSnp4us2fPlrFjxx73++Xm5srVV18tDz74oCxbtkz27dtn6f0CQFkEf6nPbSjSrsGgtve9ubWjgkAdNLCyHyKPpQHgkCFDQn6O8Fq7MW/ePGnZsqW0aNFCBg0aJCNGjJAxY8YUjOjq/s4l0a+ZMWNGwfFDDz0ktWvXlqFDh5oAEADCiU7z6shfSZbP2yKNz0iUqChn7HqlM0ZW9kPksS0JxOv1hmzXICI2NlYqVqxo16XD3p+u3fB4zHmtlG7HdLBO+2oQp/r27Sv79+832/r16tXLtK1bt67Er4+Pjy/4fPny5eb9/uxrAMCpdm/ZFzTtG8qBvTmmX70WNcQJdLmQzhjpoEHI3yUej9leTfvBnWwLABMSEkpcA1i/fn259tprTbZwVBS5KE5Zu7Fp0yZZtWqVvPvuu+Y4JiZGBg4caII4fwDYtGnTY3qvX3/91ewFPXPmTLMuFADC0UFvjqX9yoIODuhyITNjpL+LA4PAo7+b9XxZrCmHywJAXTd2zz33mCCvc+f8IEUDi5dfflnuvfdeycrKkieffNKMBp7I+rJIVJ5rNzTQO3LkSFDSh8/nM9+nadOmSfXq1Y95ClgLgWvyx0UXXVRwLi8vryCw1GCzSZMmlv8ZAMBKVeJjLe1XVswyoalTiq4lT0qiDiDsCwA10NPdQAYMGFDQpoFAmzZt5LnnnpPFixfLySefLI888ggBoEPWbmjgN2fOHPN9611ofaGWbnnjjTdk2LBhxzwFrOsI//vf/wad0+BfRwanTp0qDRo0sPT+AcAOdZolmGzfkqaBq9aINf2cRoNAXS7ETiAoswBwxYoVQYkAfu3atTM15lSPHj3kxx9/tOsWwlZ5rd2YP3++7N271yRr6EhfoMsuu8yMDmoAeKxTwJUqVZLWrVsXWRqgCrcDgFNpYoeWegmVBezXY0AzxySAFKbTvJR6QWG2Lb7T0R0NGArTNv/Iz88//2zqBSL02o38g0J/odi4dkO/NykpKUWCP38AqGVh1q9fz7cLgOtoiRct9aIjgYVH/pxWAgYo1xFAXd93xRVXyEcffSSdOnUybRpAfPfdd/Kvf/3LHK9evdokGMAZazc++OCDYs/pOk5dC2jF2lAACEca5GmpF5MV7M0xa/502tepI39ASTw+K36rF2P79u1mvZ/uDqK0rtzNN98sjRo1kkigpW50tEzLpASWPlGHDh0yf/7GjRubqdBw2wkkHFn1zAEAkc1bwu9vt7BtBFDpL+IJEybYeYmIx9oNAAAQVgGgbvmlpV8yMzMLyn/4DR482M5LAwAAoKwDQF1Ppvu/6r6yOrwaWBRaPycABAAAiLAs4DvvvFOuv/56EwDqSKCWF/G/fvnlF7suCwAAgPIKAHfu3Cm33XabxMXF2XUJAAAAOCkA7NOnjyn7AgAAAJesAezXr5/cfffdsnHjRrP9W4UKFYLO9+/f365LAwAAoDwCwBtvvNF8fOihh4qc0ySQ3Nxcuy4NAACA8ggAC5d9AQAAQISvAUR4W7lypURHR5up/BM1ZcoUswtM5cqVzT7Qd9xxh9m1AwAARGAh6IMHD8pnn30mP/74oxw+fDjonGYI48/l5fnKZd/JWbNmyfDhw83HXbt2Sd26dY/rfV5//XUZPXq0vPjii9KtWzezLeC1115rlgFMnjzZ8vsGAADlGACuXbtWLrjgAsnOzjaBYM2aNWXPnj2mLEzt2rVLHQBOnz5dnnjiCUlPT5czzjhDnnnmGencuXOx/d966y2577775IcffpBmzZrJxIkTzf0E+vbbb2XUqFEmSD1y5Ii0atVK3n77bTn55JPFCbatzZRlc7fIwX05BW1VEmKl58BmZlNyu2jtxrlz55osbn3es2fPlrFjxx7Xe61YsUK6d+8uf/vb38yx7gN91VVXyZdffmnxXQMAgHKfAtZpvosuusgUftapvy+++EJ27NghHTp0kCeffLJU76XByMiRI2XcuHGSlpZmAkAtM6NbzBUXdGiQMXToUBOIXnLJJea1YcOGgj7btm2THj16SMuWLWXp0qWyfv16EzBWqlRJnBL8pT63ISj4U3qs7XreLvPmzTPPRadtBw0aZEbvfD5fwfmqVauW+Bo2bFhBXx31++qrr8yWgOr777+XBQsWFAnGAQBA2fH4An+zWyghIcGM8mgQoZ/rmrJTTz3VtA0ZMkS+++67Y36vLl26SKdOnWTatGkFCSa6lkynKHV6sbCBAweaUcf58+cXtJ155pnStm1bmTFjhjm+8sorTWmaV1555bj/jF6vV6pXry779+83290F0jVu27dvl8aNG5c6qNRp3zljVxQJ/gJVrREr1zzSzZbpYB2xGzBggNx+++1mZLROnTpmRLVXr17m/NatW0v8en0WOsrr9/TTT8tdd91lgkh9Pw0Qn332Wcvv+0SeOQDAPbwl/P52C9tGADW4iorKf3sNBnQdoNIH/tNPPx3z++jaQR1BSklJKWjT99VjDSpD0fbA/kpHDP39NYD88MMPpXnz5qZd70+DzPfee0+cwKz5KyH4Uwf25ph+Vtu0aZMZrdMRVBUTE2MCal0L6Ne0adMSX4HBn46uPvroo/J///d/ZvT2nXfeMc/+4YcftvzeAQBAOa8BbNeunaxevdqsvzv77LPl/vvvN2sAdcStdevWx/w++jVaMzApKSmoXY+LG0XUdWuh+mu70qljXec2YcIEGT9+vFkfmJqaKpdeeqksWbLE3G8oOTk55hX4Lwg7aMKHlf1KQwM9HaULTPrQkbvY2FgzAqsBvE7zlkSnjf0jrTqtfs0118gNN9xgjrUouI7O3nTTTXLPPfcU/CMBAABEQACooz6//vqr+fyRRx6RwYMHyy233GICQl1TVp78NQovvvhis1ZR6fSwrh3UwKW4APCxxx6TBx980Pb702xfK/sdKw385syZI5MmTZLevXsHndM1lG+88YaZvl23bl2J7xM4nK5JQIWDPC0vo2xafQAAAMorAOzYsWPB5zolqCNsx6NWrVomYMjIyAhq1+Pk5OSQX6PtJfXX99SpTc36DaRrFJcvX17svYwZM8YkowSOAOpaRKtpqRfN9v2zNYDaz0q6ZlKTdjR5Rkf6Al122WVmdFADQJ3mPVaaCKTlXnREWKfZdf2gjgpquz8QBAAAZcvx828VK1Y0mcOLFy8OGsHT465du4b8Gm0P7K8++eSTgv76nppUouvdAmmNuoYNGxZ7LzoNqqNbgS87aGKHlnopSY8BzSxPANEAT9dOFg7+/AGgloXRbOnSuPfee+XOO+80HzXg1uBS110+99xzFt45AABwxAigjrhp5qcGYrrmrvB0X2n2AtZRN80c1lFFrf2nO0voOrLrrrvOnNfp5Xr16pkpWqXZqzqNq1OZupPFm2++aYKX559/vuA97777bpPccNZZZ8k555xjRig/+OADk7TgBFrnr+/NrYvUAdSRPw3+7KgDqH/+4uhzP54pWx1p1fI9+gIAABEeAOpuD5r5q9N9WkZEd344XhqoZWVlmUQSTeTQ9XoasPkTPfQ6gevMtPac7kCho05awFjXHWqGb2DyyV//+lez3k+DRi1KreVqtAi01gZ0Cg3yGp+RWC47gQAAgMhlWx3AatWqybJly0ywFqnsqgOI48MzBwAcCy91AO0bAdTkCLI8AQCRxJebK9lrvpIjWVkSk5gocR07iIeENoQh2wJAXaenu3ToYn/d/xUAgHDmXbhQMh59TI4crSmrYpKTJWnsGIkvVDoLcFUAWKNGjaC1fpqo0aRJE4mLizM7gwT65ZdfrLw0AAC2Bn87bx+hBUyD2o9kZOS3T51CEAj3BoA66ofQRadhP541ALumfXXkr3Dwl3/SJ+LxmPPVzjuP6WC4MwDUUi2QglqDmpm8a9cuSUxMNMcnkgmN4ulaU90zWjPF9ZnrswYAq5g1fwHTviH+EjLntV+VLp158HDnGkANeHTnBy3ZUjgzVrNlde9drQ9YeK/eSKOBiGYA79692zwT2E+XGpx88snsLwzAUprwYWU//LncvFxJy0yTrOwsSYxLlPa120t0FLtHOToA1OBP06tD7ZKhJVN0f2DtM3HiRIl0OhKlAYnusVuawtcoPd1WTotOM8oKwGqa7WtlP5Rs0Y5FMmHVBMnI/mNL16S4JBndebSkNEzh8Tm1DqAWW9YCy8UVVF6xYoXceOON8s0330i4o44QALhjDeDW81JMwkfIdYAej8QkJUnTxYtYA2hB8Ddy6UjxSfBz9kj+EqrJvSZbEgR6qQNo/V7AWvxYR72KU79+ffnhhx+sviwAIMzk5flk56a9snl1uvmox06kdf601Ev+QaG13EeP9Tz1AE982ldH/goHf8rfNnHVRNMPDpwCrly5sgnwigsC9Zz2AQC417a1mUX2Oq+SECs9B9qz1/mJMnX+pk4pWgcwKYk6gBbRNX+B076hgsD07HTTr1NyJ6su61qWB4BdunSRV155Rc4666yQ5+fMmSOdO5MlBQBuDv5Sn9tQpF2DQW3ve3NrxwaBWuqFnUDsoQkfVvZDGQeAmuF7/vnnm4SPu+++uyDbNyMjQx5//HGZPXu2LFy40OrLAgDCgE7z6shfSZbP2yKNz0iUqCjnlc7SaV5KvdhDs32t7IcyXgN4zjnnyPTp02XatGlSt25dsztIzZo1zefa/swzz8i5555r9WUBAGFg95Z9QdO+oRzYm2P6wV201Itm+/oTPgrT9uS4ZNMPDt0L+Oabb5YLL7xQ5s2bJ1u3bjWFeps3by6XX365SQIBALjTQW+Opf0QObTOn5Z60SxgDfYCk0H8QeGozqOoB+jkAFDVq1dP7rjjDrveHgAQhqrEx1raD5FFS7xoqZdQdQA1+KMOYBgEgAAAFFanWYLJ9i1pGrhqjVjTD+6kQd45Dc5hJxCbEQACAMqMJnZoqZdQWcB+PQY0c2QCCMp2OphSL2GWBAIAQEm0xIuWetGRwMIjf04tAQNEGkYAAQBlToM8LfVisoK9OWbNn077MvJnPd05Q4sna/08LaGiWbQ6wgZ3IwAEgAiprxduwZTeX70WNcr7NiJ+b91QCRWabUtChbuVSwDYuHFjUwvw4YcfNvUBAQDu2VYNZRf8aUmVwnvrZmZnmnbNtiUIdK9yWQM4ZMgQyc3Nle7du5fH5QEg4rZVK5xV699WTc/DndO+OvJXOPhT/raJqyaafnCnchkBfOCBB8rjsgAQUcJ9WzVfbi776tpE1/wFTvsWefbik/TsdNOPbFt3Yg0gALhgWzWnrbXzLlwoGY8+JkfS0wvaYpKTJWnsGInv3btc7y0SaMKHlf0QeWwJAPPy8uSzzz6TZcuWyY4dOyQ7O1sSExOlXbt2kpKSIg0aNLDjsgDgKuG6rZoGfztvHyHiC56ePJKRkd8+dQpB4AnSbF8r+yHyWLoG8LfffpPx48ebAO+CCy6Qjz76SPbt2yfR0dFmT+Bx48aZBBA998UXX1h5aQBwnXDcVk2nfXXkr3Dwl38yv03Paz8cPy31otm+/j10C9P25Lhk0w/uZGkA2Lx5c1m/fr3MnDlTvF6vrFy5Ut5++2159dVXZcGCBfLjjz/Ktm3bpGfPnnLllVeafgCAE9tWrSRO21Yte81XQdO+Rfh85rz2w/HTOn9a6kUVDgL9x7q3LvUA3cvSAHDhwoUyb948M8JXoUKFkH0aNmwoY8aMkS1btphSMACAE9tWrSRO21btSFaWpf1QPC3xoqVeascFlwLSkUFKwMDSNYCnnnrqMffVALFJkyZ8BwDAgm3VCtcB1JE/Df6cVgcwJjHR0n748yDwnAbnsBMI7E8C0WneY3HyySdbfWkAcKVw2lYtrmMHk+2rCR8h1wF6PBKTlGT6wRo6zUupF9geAGqSh5/v6H/cHo8nqE2PtRA0AMBd26p5oqNNqReT7au/GwKDwKO/K/S89gMQRgGgBnf169eXa6+9Vi666CKJiaHUIADgD6bO39QpResAJiVRBxAoIx6ff5jOIunp6fLyyy/LSy+9ZErADBo0SIYOHVqq9YHhQjOdq1evLvv375f4+Pjyvh0ACCvsBILy4uX3t/UBYKDly5ebQPCtt96SVq1amUBQX1FR5bIFseX4AQIAIPx4CQCtLQNTWI8ePWTWrFmm5EtcXJwMGzbMjAoCAAAgQgPAFStWyA033GAKRB84cECmT58uCQnOKUgKAADgRpZnaOzevVvmzJljpn737t0rV199tXz++efSunVrqy8FAAAAJwSAWt+vXr16MmTIEOnfv78p+JyXl2e2iAt0+umnW31pAAAAlEcSSGCCh7/+X+FLREodQBaRAgAQfrwkgVg/Arh9+3ar3xIAAABODgAbNmxo9VsCAADAQrZu03Ho0CGz9i8zM9OsAwyk6wMBAAAQQQFgamqqDB48WPbs2VPkXKSsAQQAAAhHttUBHD58uFxxxRWmLIyO/gW+CP4AAAAicAQwIyNDRo4cKUlJSXZdAgCAMpWblytpmWmSlZ0liXGJ0r52e4mOiua7gLBjWwB4+eWXy9KlS6VJkyZ2XQIAgDKzaMcimbBqgmRkZxS0JcUlyejOoyWlYQrfCbi7DqBfdna2mQJOTEyUNm3amILQgW677TYJd9QRAgD3BH8jl44UnxSqayv59W4n95pMEBhGvNQBtC8AnDVrlgwbNkwqVaokJ510UkFRaKWff//99xLu+AECAHdM+/Z5u0/QyF/hIFBHAlMvS2U6OEx4CQDtmwK+55575MEHH5TRo0cH7Q4CAEA40TV/xQV/SkcF07PTTb9OyZ3K9N6A42VbZHb48GEZOHAgwR8AIKxpwoeV/YCIDgCHDBkic+fOtevtAQAoE5rta2U/IKKngLXW3+OPPy4ff/yxnH766UWSQCZPnmzXpQEAsIyWetE1fpnZmUWSQALXAGo/QNweAP73v/+Vdu3amc83bNgQdC4wIQQAACfTOn9a6kWzgDXYCwwC/VnAozqPIgEEYcW2LGA3IIsIANxdBzA5LtkEf9QBDC9esoDtGwEEACCSaJB3ToNz2AkEEcHSJBCt+/e///3vmPpqgshrr71m5eUBALB9OlhLvVxwygXmI9vAIVxZOgKou36cdtpp0r17d7noooukY8eOUrduXVMMeu/evbJx40ZZvny5vPnmm6b9+eeft/LyAAAAKI81gBkZGfLCCy+YIE8DvkDVqlWTlJQUueGGG6Rv374S7lhDAABA+PGyBtDeJBAd9fvxxx/lt99+k1q1akmTJk0iKgOYHyAAAMKPlwDQ3iSQGjVqmBcAAACcg016AQAAXIYAEAAAwGUIAAEAAFyGABAAAMBlCAABAABcxrYAUOsBXnPNNabgc0xMjERHRwe9AAAAEGFlYK699lpTA/C+++6TOnXqRFT9PwAAgHBmWwCoW74tW7ZM2rZta9clAAAA4KQp4AYNGoiNm4wAAADAaQHglClTZPTo0fLDDz/YdQkAQBjLzcuV1emrZcH3C8xHPQYQ5gHgwIEDZenSpWb/32rVqknNmjWDXsdj+vTp0qhRI6lUqZJ06dJFVq1aVWL/t956S1q2bGn6t2nTRhYsWFBs32HDhpl1ihq4AnC3vDyf7Ny0VzavTjcf9djpfLm5cvDLVbJ//ofmox472aIdi6TP233k+o+vl1HLRpmPeqztAMJ4DaDVgdTcuXNl5MiRMmPGDBP86fv36dNHNm3aJLVr1y7Sf8WKFXLVVVfJY489JhdeeKG8/vrrcskll0haWpq0bt06qO+7774rX3zxhclYBuBu29ZmyrK5W+TgvpyCtioJsdJzYDNp0q7o3zVO4F24UDIefUyOpKcXtMUkJ0vS2DES37u3OI0GeSOXjhSfBAfWmdmZpn1yr8mS0jCl3O4PcAOPL0wW6mnQ16lTJ5k2bZo5zsvLM+sMhw8fbqaaQ41AHjx4UObPn1/QduaZZ5qkFA0i/Xbu3Gne++OPP5Z+/frJiBEjzOtYeL1eqV69uuzfv1/i4+Mt+XMCKN/gL/W5DcWe73tza8cFgRr87bx9hEjhv8qPVl6oN3WKo4JAnebVkb6M7IyQ5z3ikaS4JEm9LFWioygZBnt4+f1tbyHo3Nxcefvtt2X8+PHmpSNt2lZahw8flq+++kpSUv74F2FUVJQ5XrlyZciv0fbA/kpHDAP7axCptQrvvvtuOe200/70PnJycswPTeALQGTQaV4d+SvJ8nlbHDUdrNO8OvJXJPgzJ/Pb9LyTpoPTMtOKDf6UjgqmZ6ebfgDCMADcunWrnHrqqTJ48GB55513zGvQoEEm0Nq2bVup3mvPnj0mcExKSgpq1+P0gCmPQNr+Z/0nTpxoilTfdtttx3QfOp2sI37+l45AAogMu7fsC5r2DeXA3hzTzymy13wVNO1bhM9nzms/p8jKzrK0HwCHBYAaVGkCyE8//WTW3elLC0M3btz4mAMuO+mI4tSpU2X27NnHXKR6zJgxZrrX/9I/G4DIcNCbY2m/snAkK8vSfmUhMS7R0n4AHJYE8tlnn5nEisCM35NOOkkmTJgg3bt3L9V71apVy2wfp9vLBdLj5OTkkF+j7SX11yLVmZmZcvLJJxec11HGO++80ySYhCpfExsba14AIk+V+FhL+5WFmMRES/uVhfa125s1fprwUTgJJHANoPYDEIYjgBoo/frrr0XaDxw4IBUrVizVe2n/Dh06yOLFi4PW7+lx165dQ36Ntgf2V5988klBf137t379elm3bl3BS7OAdT2gJoQAcJc6zRJMtm9JqtaINf2cIq5jB5Pt60/4KMLjMee1n1NoYsfozqMLgr1A/uNRnUeRAAKEawCopVduuukm+fLLL82OIPrSEUGtt9e/f/9Sv5+WgJk5c6a8/PLL8u2338ott9xisnyvu+46c17XGuoUrd/tt98uqampMmnSJPnuu+/kgQcekDVr1sitt95aMBqp5WACXxUqVDAjhC1atLDwSQAIB1FRHlPqpSQ9BjQz/ZzCEx1tSr3kHxS6r6PHel77OYmWeNFSL7XjgjOqdeSPEjBAmE8BP/300zJkyBAz4qaBlTpy5IgJ/nTtXWlpWZesrCy5//77TSKHlnPRAM+f6KHrCzUz2K9bt26m9t+9994rY8eOlWbNmsl7771XpAYgAPhpiRct9VK4DqCO/Gnw57QSMMqUeJk6pWgdwKQkx9YB9AeB5zQ4x2T7asKHrvnTaV9KvwARUgdwy5YtZgROaVZw06ZNJVJQRwiITFrqxWQFe3PMmj+d9nXSyF8oWurFZAVnZZk1fzrt67SRP8ApvNQBDJ9C0E7EDxAAAOHHSwBo7RSwrtN7+OGHpUqVKubzkkyePNnKSwMAAKA8AsC1a9fK77//XvA5AIQjplMBRDqmgE8AQ8hA5NG9dYskVCQnOzqhAkDpeJkCtq8MzPXXXx+yDqCWbtFzAODE4G/n7SOKbK92JCPDtOt5AIgEto0A6s4du3fvltq1axfZ11dr7WlJmHDHvyCAyJr23XpeSvF762pR5aQkabp4Edm1QJjzMgJofR1Afaj+ws86AlipUqWgrdYWLFhQJCgEgPJmSqgUF/wpn8+c135VunQuy1sDTkxersiOFSIHMkSqJok07CYS5fASQeF4z24PABMSEsTj8ZhX8+bNi5zX9gcffNDqywLACdH6eVb2Axxh4/siqaNEvLv+aIuvK9J3okir0u/KVSbC8Z7DkOUB4JIlS8zo37nnnitvv/221KxZM2hP34YNG5o9dwHASbR4spX9AEcEUvMG6/B1cLt3d377gDnOC6jC8Z7DlOUB4Nlnn20+bt++XRo0aBC0PRsAOJXunKHZvprwodO9xa0B1H6A4+kUqo6iFQ6kDG3ziKSOFmnZzzlTq+F4z2HMtuhMR/o0+MvOzjZbwa1fvz7oBQBOotumaamX/INC274dPdbzbK+GsKDr5wKnUIvwiXh35vdzinC85zBm+QigX1ZWllx33XXy0UcfhTyvCSEA4CSmzt/UKUXrACYlOb4OYG5erqRlpklWdpYkxiVK+9rtJZpREvfS5Akr+5WFcLznMGZbADhixAjZt2+ffPnll9KrVy959913JSMjQ8aPHy+TJk2y67IAcEI0yKt23nn5WcFZWWbNn077Onnkb9GORTJh1QTJyP7jF2NSXJKM7jxaUhqmlOu9oZxo5qyV/cpCON5zGLMtAPz000/l3//+t3Ts2NFMBeuU8Pnnny/x8fHy2GOPSb9+/ey6NACcEA32wqXUiwZ/I5eOFF+hdVOZ2ZmmfXKvyQSBbqRlUzRzVpMnQq6p8+Sf135OEY73HMZsWwOoO3746/3VqFHDTAmrNm3aSFpaml2XBQDX0GlfHfkrHPwpf9vEVRNNP7iMTv9r2RSj0JpW/3HfCc5KpgjHew5jtgWALVq0kE2bNpnPzzjjDHnuuedk586dMmPGDKlTp45dlwUA19A1f4HTvqGCwPTsdNMPLqTlUrRsSnyh37k6iubUcirheM9hyrYp4Ntvv91sBafGjRsnffv2lddee83UApw9e7ZdlwUA19CEDyv7IQJpwKRlU8JpV41wvOcwZFsAOGjQoILPO3ToIDt27DDlYE4++WSpVauWXZcFANfQbF8r+yFCaeDUuKeElXC85zBjyxTw77//Lk2aNJFvv/22oC0uLk7at29P8AcAFtFSL5rt6ymyXiqftifHJZt+AGB7AFihQgU5dOiQHW8NIMzk5flk56a9snl1uvmox7CG1vnTUi+qcBDoPx7VeRT1AAEU4fHpxr02ePTRR2Xz5s3ywgsvSEyMbTPN5crr9Ur16tVl//79prwNgGDb1mbKsrlb5OC+nIK2Kgmx0nNgM2nSLr9KAOypA6gjfxr8UQcQ4Pd3mQaAf/3rX2Xx4sVStWpVU/qlSpUqQeffeecdCXcEgEDJwV/qcxuKPd/35tYEgRZiJxDg2HkZwLEvCSQhIUEuu+wyfh4BF9JpXh35K8nyeVuk8RmJEhUVev0aSj8d3Cm5E48NQPkGgC+99JJdbw3A4XZv2Rc07RvKgb05pl+9FjXK7L4AADYXgj733HPNXsChhl31HIDIddCbY2k/AECYBIBLly6Vw4cPF2nX7OBly5bZdVkADlAlPtbSfgAAh08Br1+/vuDzjRs3Snp6esFxbm6upKamSr169ay+LAAHqdMswWT7ljQNXLVGrOkHAIiAALBt27bi8XjMK9RUb+XKleWZZ56x+rIAHEQTO7TUS0lZwD0GNCMBBAAiJQDcvn27aGWZU045RVatWiWJiX9sQaT7ANeuXVuio9nPD4h0WudPS70UrgOoI38a/FEHEGEpL5c9ahERLA8AGzZsaD7m5eVZ/dYAwowGeVrqxWQFe3PMmj+d9qX0C8LSxvdFUkeJeHf90RZfV6TvRJFW/cvzzoBSs3WLji1btsiSJUskMzOzSEB4//3323lpAA6hwR6lXhARwd+8wSJSaO8E7+789gFzCAIRVmwLAGfOnCm33HKL1KpVS5KTk82aQD/9nAAQABA207468lc4+DO0zSOSOlqkZT+RKJY4weUB4Pjx4+WRRx6RUaP0PxoAAMLUjhXB075F+ES8O/P7Ne5ZhjcGOLAO4N69e+WKK66w6+0BACgbBzKs7QdEcgCowd/ChQvtensAAMpG1SRr+wGRPAXctGlTue++++SLL76QNm3aSIUKFYLO33bbbXZdGgAA6zTslp/tqwkfIdcBevLPaz8gTHh8WrTPBo0bNy7+oh6PfP/99xLudF/j6tWry/79+yU+Pr68bwcAYHsWsAr8tXk0wZEs4LDi5fe3fSOAWhAaAICIKKqsdf40yAtZB3ACJWAQdmytA6gOHz5sgsEmTZpITIztlwMAhINwLKqs96WlXsIlaAXKIwkkOztbhg4dKnFxcXLaaafJjz/+aNqHDx8uEyZMsOuyAIBwmU4tXFrFX1RZzzuVBnta6qXN5fkfCf4QpmwLAMeMGSNff/21LF26VCpVqlTQnpKSInPnzrXrsgAcxpebKwe/XCX7539oPuoxXOxPiypLflFl7QfANrbNyb733nsm0DvzzDODdgHR0cBt27bZdVkADuJduFAyHn1MjqSnF7TFJCdL0tgxEt+7d7neG8oJRZWByB4BzMrKktq1axdpP3jwYFBACCByg7+dt48ICv7UkYwM067n4UIUVQYiOwDs2LGjfPjhhwXH/qDvhRdekK5du9p1WQAOoNO8OvInoapMHW3T80wHuxBFlYHIngJ+9NFH5S9/+Yts3LhRjhw5IlOnTjWfr1ixQj777DO7LgvAAbLXfFVk5C+Iz2fOa78qXTqX5a2hvFFUGYjsEcAePXrIunXrTPCnO4HotnA6Jbxy5Urp0KGDXZcF4ABHsrIs7YcIolmzWurFKLwc6Oix1tUjuxawla2F+bT238yZM+28BAAHiklMtLQfIgxFlYHIDQAXLFgg0dHR0qdPn6D2jz/+WPLy8sz0MIDIFNexg8n21YSPkOsAPR6JSUoy/eBSFFUGInMKePTo0ZIbot6Xbj2s5wBELk90tCn1kn9QaJrv6LGe135wMYoqA5EXAG7ZskVatWpVpL1ly5aydetWuy4LwCG0zl+9qVPMSF8gPdZ26gACQAROAVevXl2+//57adSoUVC7Bn9VqlSx67JARMvL88nuLfvkoDdHqsTHSp1mCRIV5dy6mhrkVTvvvPys4Kwss+ZPp32dPvKXm5craZlpkpWdJYlxidK+dnuJJikBQASxLQC8+OKLZcSIEfLuu++aZBB/8HfnnXdK//4O3egbcLBtazNl2dwtcnBfTkFblYRY6TmwmTRpV7ToulNosBdOpV4W7VgkE1ZNkIzsjIK2pLgkGd15tKQ0TCnXewPcIjfPJ6u2/yKZvx6S2tUqSefGNSXawf/YDUceny7Ks8H+/fulb9++smbNGqlfv75p+9///ic9e/aUd955RxISEiTceb1eM9Kpf9b4+Pjyvh1EePCX+tyGYs/3vbm1o4PAcAr+Ri4dKb5C+9R6jpYnmdxrMkEgYLPUDbvlwQ82yu79hwra6lSvJOMuaiV9W9ex5Bpefn/bFwAqfetPPvlEvv76a6lcubKcfvrpctZZZ0mk4AcIZTXtO2fsiqCRv8Kq1oiVax7p5ujpYKfTad8+b/cJGvkrHATqSGDqZalMBwM2Bn+3vJpW6J9gf1SMfHZQe0uCQC8BoL11AHX7t969e5sXgONj1vyVEPypA3tzTL96LWrwmI+TrvkrLvhTOiqYnp1u+nVK7sRzRtgIl+lUvU8d+Qs1KqVtesd6/vxWyY68/3BjawC4ePFi88rMzDS1/wK9+OKLdl4aiBia8GFlP4SmCR9W9gPcMp1qFQ1SA+8zVBCo57Vf1yYnlem9RSLbysA8+OCDZuRPA8A9e/bI3r17g14Ajo1m+1rZD6Fptq+V/cpcXq7I9mUi//1X/kc9hqv5p1MLB1Xp+w+Zdj3vJDpCaWU/lNMI4IwZM2T27NlyzTXX2HUJwBW01Itm+/7ZGkDth+OnpV50jV9mdmaRJJDANYDaz3E2vi+SOkrEu+uPtvi6+Xvu6o4bcJ1wnE7V6Wkr+6GcRgAPHz4s3bp14/kDJ0gTO7TUS0l6DGhGAsgJ0jp/WupFfz16CuXG5R/7ZFTnUc5LANHgb97g4OBPeXfnt+t5uE5pplOdQtcm6vR0ceGotut57QcHB4A33HCDvP7663a9PeAqWuJFS73oSGDhkT9KwFgn5WC2TM7YI7ULbWOZlJtr2vW8o+g0r478FTvOo/OAo5kOtnhkbeW2n+Xf63aaj3rsROE4naojkbo2URUOAv3Het4pI5bhzrYp4EOHDsnzzz8vixYtMuVfKlSoEHR+8uTJdl0aiNggsPEZiWG1E0hYORpMpWRnyznZ2ZJWKVayoqMlMTdX2h/KkWj9FaTBVMt++XvYOsGOFUVH/oL4RLw78/s17lmGNxaZwimhIlynU/U5aqmXws852aHPOZzZFgCuX79e2rZtaz7fsGFDkfIwAEpPgz1KvdgfTGl41+lQjvODqQMZ1vZDqevT+RMqrKpPZ/V0qt5fqDFKz9GgyonTqfocdW1iOJSuCWe2BYBLliyx660BwHrhGExVTbK2HyImocI/narBqd6RL8ymU/W+KPUSpmsAA+kWcPoCAMcKx2CqYbf8bN+Sls3H18vvB1clVAROp+pIXyA9dtqIJSJoBFALP48fP14mTZokBw4cMG3VqlWTO++8U+655x6JiiqT2BMAShdMafZscZNmet5JwZSuRdRSL5rtW9w4T98JzlmzGKbCMaHCj+lUlHkAqEHerFmzZMKECdK9e3fTtnz5cnnggQdMgsgjjzxi16UBwD3BlNb5GzCnmDqAE6gD6OKECj+mUxGKx+crVPDKInXr1jXFoPv3Dy5C+u9//1v+/ve/y86dOyXcsZk0EIFCFlWu5/xgSrOYNUFF1yjqNLWOVDotWA3jNYA9Jn76pwkVy0ed69g1dQjm9XqlevXqsn//fomPj3fl47FtBPCXX36Rli1bFmnXNj0HAI6kQZ6Wegm3YErvzynZyREm3BMqgFBsW4h3xhlnyLRp04q0a5ueAwDHB1NtLs//6PTgD7YjoQKRxrYA8PHHH5cXX3xRWrVqJUOHDjUv/Vz3B37iiSeO6z2nT58ujRo1kkqVKkmXLl1k1apVJfZ/6623zIij9m/Tpo0sWLCg4Nzvv/8uo0aNMu1VqlQxU9aDBw+WXbtKKqoKAHBzEKjTvG/ceKZMvbKt+ajHZNMiHNkWAJ599tmyefNm+etf/yr79u0zr0svvVQ2bdokPXuWfppi7ty5MnLkSBk3bpykpaWZUcQ+ffpIZmZmyP4rVqyQq666ygSea9eulUsuucS8/EWps7XSf1qa3HfffebjO++8Y+6t8JpFAAAKJ1Rc3Lae+ci0L8KVbUkgVtMRv06dOhVMK2uZmQYNGsjw4cNl9GjdwD3YwIED5eDBgzJ//vyCtjPPPNPsTqLJKaGsXr1aOnfuLDt27JCTTz75T++JRaQAAIQfL0kg1o8AbtmyxYy86cMtTLNt/va3v8n3339fqvc8fPiwfPXVV5KSklLQpnUE9XjlypUhv0bbA/srHTEsrr///nSbuoSEhJDnc3JyzJ8r8AUAACBuDwB1fZ+OzIVKq9aUaz1X2jWAe/bskdzcXElKCq7Ar8fp6ekhv0bbS9NfaxPqmkANXotLCX/sscfMn8H/0j8LAACAuD0A/Oyzz+SKK64o9vyAAQPk008/FSfRhBC9L50Nf/bZZ4vtN2bMGDNK6H/99NNPZXqfQDjKzcuV1emrZcH3C8xHPQYARFgdwB9//FFq165d7PlatWqVOnDSr4mOjpaMjOBN2PU4OTk55Ndo+7H09wd/uu5PA9OSCkLGxsaaF4Bjs2jHIpmwaoJkZP/x32JSXJKM7jxaUhoGL9EAAITxCKBOjW7btq3Y81u3bi111e2KFStKhw4dZPHixQVtmgSix127dg35Ndoe2F998sknQf39wZ+uW1y0aJGcdNJJpbovACUHfyOX3iEZB4OXXWQeTDfteh4AECEB4FlnnSXPPPNMseeffvrp4yoDoyVgZs6cKS+//LJ8++23csstt5gs3+uuu86c1xp+OkXrd/vtt0tqaqpMmjRJvvvuO7MH8Zo1a+TWW28tCP4uv/xy0/baa6+ZNYa6PlBfmnQCOJEvN1cOfrlK9s//0HzUYyfSad4Jn48zyyrEE7w7gk+PfT6ZuOIBpoMBIFKmgDUI01E2Da7++c9/SosWLUy7BmFaHPrjjz82NfpKS8u6ZGVlyf3332+CNC3nogGeP9FDp541M9ivW7du8vrrr8u9994rY8eOlWbNmsl7770nrVu3Nud1L+L333/ffK7vFWjJkiXSq1evE3oOgNW8CxdKxqOPyZGARKaY5GRJGjtG4nv3dtQDT0tfLRm/e4sEf4FBYPrh/aZfp7pnlvn9AYDb2VIHUGvvXX/99fLzzz8HtesU6wsvvBAxxZapI4Qy+1lbuFB23j7CjJwFORpg1Zs6xVFB4IIvJ8uo7176034TW14nF3QZWSb3BAB+XuoAWj8CqC688EKTVKEjdLrmT2PM5s2bS+/evSUuLo6fQKAUdJpXR/70v6Mi42k+n9mYXs9XO+888UQ7Y8/axNw8S/sBAMIgAFSVK1c228ABODHZa74y076hJ1PFtOt57VelS2dHPO72yZ0k6dtZkhkdnb/mrxCPzydJubmmHwAggvYCBmCNw5kZlvYrC9GNesjo3zwFwV4g//Go3zymHwCg7BEAAg63PWavpf3KRFS0pJw7QSZn/iy1C2Uq68iftut57QcAiKApYADWSG9aU6pVE6n5a+h/sekqul+qifzatKazHnmr/qKlns9JHSVphzMkKzpaEnXat2Itib7wOXMeAFA+CADhWnl5Ptm9ZZ8c9OZIlfhYqdMsQaKiiltpV34SqyXJjPOj5M538kywFxgE6rHe8ezzo2RYteC9rx2hVX+JbtlPOu1YIXIgQ6RqkkjDboz8AUCkBYCaWn0sSrsbCGClbWszZdncLXJwX05BW5WEWOk5sJk0aVf8VobloX3t9rKjXR2ZLOky5JNcqfXrH+d05O/l86Plx3Z1TD9H0mnexqUv/g4ACKMAMCEhQTzFFH9VppSFx2N23gDKK/hLfW5DkXYNBrW9782tHRUERkdFm71zR2aPlNXNPNLypzypcUBkb1WR7xpEiS/KI5M7jzL9AAAolwBQd9EIDPYuuOACU/y5Xr16Vl8KOK5pXx35K8nyeVuk8RmJjpoOTmmYIpN7TZYJqybIxoZ/ZPsmxyXLqM6jzHkAAMotADz77LODjqOjo+XMM8+UU045xepLAaVm1vwFTPuGcmBvjulXr0UNRz1hDfLOaXCOpGWmSVZ2liTGJZppX0b+AAClRRIIXEUTPqzsV9Y02OtE8WQAwAmiDiBcJa5qBUv7AQAQjsokACwpKQQoS9X3bZXYQ3vNHroh+XwSe+gX0w8AgEhl+RTwpZdeGnR86NAhGTZsmFSpUiWo/Z133rH60sCfyvt5jzTb+pZsOO3G/CAw8B8nR4PCZlv/JXk/X8PTBGyWm+eTVdt/kcxfD0ntapWkc+OaEu2g5CsgklkeAFavXj3oeNCgQVZfAjhuMYmJUnvP19L6m5mypekVklPpj0SP2Jy9JvjT8zGJI535lPNyRSiqjAiQumG3PPjBRtm9/1BBW53qlWTcRa2kb+s65XpvgBt4fFqrBcdd9FoD3v3791PYOkz4cnNl63kpciQjwwz47UtoKjkV4yX2sFcS9m01A4IxSUnSdPEi8UQ7rK7exvdFUkeJeHf90RZfV6TvRLZVQ9gFf7e8miaFf/n4x/6eHdSeIBC28vL72541gD/88IPMnDlTpk+fLt98840dlwCOiwZ1SWPH5H/uEamxb4skZ35lPvpng/W8I4O/eYODgz/l3Z3frueBMJn21ZG/UCMP/jY9r/0AhFEAqIWgTzvtNLn55ptl+PDh0q5dO3n11Vetvgxw3OJ795af771e9lYLXmu0Nz7KtOt5x0376shfSb8yU0fn9wMcTtf8BU77hvqJ1vPaD0AYrQG877775Pzzz5dnn31WKlWqJPfee6/885//ZC0gHGPRjkUy8sgckVs8cupPUQHbqnnEd2SOTN7R1lk7a+iav8Ijf0F8It6d+f3YcxcOpwkfVvYD4JARwA0bNsijjz4qderUkRo1asgTTzwhmZmZ8vPPP1t9KaDUcvNyzXZqPv1flEc2NoySz0+LMh/zjv7XMHHVRNPPMQ5kWNsPKEea7WtlPwAOCQB1YWWtWrUKjuPi4qRy5comUQIob7qNWkZ28YGSBobp2emmn2NUTbK2H1COtNSLZvsWV+xF2/W89gMQZlvBffzxx0HlYPLy8mTx4sVmdNCvf//+dlwaKJHuoWtlvzLRsFt+tq8mfIRcB+jJP6/9AIfTOn9a6kWzgD2FfqL9QaGepx4gEIYB4JAhQ4q0aVJI4M4gubkOmmKDayTGJVrar0xEReeXetFs3+J+ZfadkN8PlqBAsb20zp+WeilcBzCZOoBA+AaAOtoHOFX72u0lKS5JMrMzzXRvYR7xmPPaz1Fa9RcZMKeYOoATHF0HMNyCKQoUl10QeH6r5LD62QAiCYWgTwCFJMM4C3hp/k4fgUGgBn9qcq/JzsoCDuOdQMItmKJAMeAOXgpB2xcAfvrpp2a/Xy0KrVO+jRs3lssvv1zOOussiRT8AIV3EKjZwIEJIclxyTKq8yjnBn9hJtyCKR2p7DHx02Jr1HmOTlEuH3Uuo1RAmPMSANoTAA4bNkyef/55UwamefPmopfYsmWL7Nu3T/7+97/LM888I5GAH6DwpqVeNNtXEz50zZ9O+0Y7eDQtnIRjMLVy289y1cwv/rTfGzeeKV2bnFQm9wTAHl4CQOvLwLz77rvy0ksvyYsvvih79uyRlStXyhdffCFZWVlmezgNDN9/n22rUP401Ov02yG54GC2+Ujo5+7dHihQDMBNLE8C0eBv5MiRcu211wa1R0VFyfXXXy+bNm2SWbNmUQYG5Uv3zg2ZUDHR0QkV4SIcg6lwL1Acbsk2ACIsAExLSzPbvxXn0ksvlcsuu8zqywKlC/5MSZVCqx+0zp62a7YtQaDrgil/geL0/YeKq7Zopq2dWKA43JJtAETgFLBO+9avX7/Y83qObeFQrlm0OvIX8lf80bbU0fn94KrdHvwFilXh+3ZygWJ/sk3hKXcNZLVdzwOA7QHg4cOHpUKFCsWej4mJMX2AcqElVAKnfYvwiXh35veD64Ipf4FiHekLpMdOy1r2T/vqyF8J/5wx57UfANi+E8h9991n9gAOJTs7245Lopzl5flk95Z9ctCbI1XiY6VOswSJctgvd0Pr51nZDxG320M4FSguTbINmcsAbA0Atc6fJnr8WR9Ejm1rM2XZ3C1ycF9OQVuVhFjpObCZNGlXWxxFiydb2Q8RE0wF0vsLh4ApHJNtAERoALh06VKr3xIOD/5Sn9tQpF2DQW3ve3NrZwWBunOGZvtqwkdxS/31vPaDq4KpcBSOyTYAInQN4J/59ttv5a677irry8KmaV8d+SvJ8nlbTD/H0ELPWuqlpNVpurcuBaERBsIx2QaAiwLAgwcPmtp/3bp1k9NOO01SU1PL4rKwmVnzFzDtG8qBvTmmn6NoiRct9RJfaA2ajvxRAgZhJFyTbQBEaBKI3+eff24Cv3nz5slvv/0md9xxh9khpGXLlnZeFmVEEz6s7FfmQWDLfvnZvprwoWv+dNqXkT+EmXBNtgEQYQFgZmamzJ492wR6+/fvl6uuusqsC+zatavZCYTgL3Jotq+V/cqcBnuNe5b3XQCuTbYBEEEBYMOGDeXyyy+XqVOnyvnnn2+2gENk0lIvmu1b0jRw1Rr5JWEA2ItkGwClEWVHALh8+XL5z3/+I5s3b7b67eEgWudPS72UpMeAZs6sBwgAgItZHgB+99138uqrr8ru3bulU6dO0qFDB3nqqafMOY+HQCDSaIkXLfVSJaFiUHuVGrHOKwEDAADsSwLp3r27eT399NPyxhtvyEsvvSS5ubny97//Xf72t7/JJZdcIomJiXwLIsT2muvl1fYTJTq9qsT9Hi/ZFbySm3xA6tccJU0kpbxvDwAAFOLx+Xy+sqr/pxnBr7zyivzyyy/y+++/S7jzer1SvXp1k+wSHx8vbrRoxyIZuXSk+AoVVfYcLUIxuddkSWnozCBQ90dl0TwAuI+X399lFwD6aeD3wQcfyKWXXirhzu0/QLl5udLn7T6SkR1631wNApPikiT1slSJdlh5ldQNu4uUzdCCuZTNAIDI53X5729V5im6FSpUiIjgDyJpmWnFBn9KRwXTs9NNP6cFf7e8mhYU/Kn0/YdMu553Kh21XLntZ/n3up3mox4DAFDuawC17MufJXvo+SNHjlh9aZSxrOwsS/uVBQ2YdOQvVNikbfqTq+e1pprTaqgxagkAcGwA+O677xZ7buXKlSYxJC8vz+rLohwkxiVa2q8s6Jq/wiN/hYNAPa/9ujY5SZw2alk4cPWPWupOEOz4AAAotwDw4osvLtK2adMmGT16tFn7d/XVV8tDDz1k9WVRDtrXbm/W+GVmZxZJAglcA6j9nEJ3SbCyX1kI51FLAIAL1wDu2rVLbrzxRmnTpo2Z8l23bp28/PLLplg0wp8mdozuPDoo69fPfzyq8yhHJYDoFllW9nPaqCUAAOUWAGpWzahRo6Rp06byzTffyOLFi83oX+vWre24HMqRlni55pT7xPN7vLTakSfdv8kzHz2H402700rA6P6omu1b3DiZtut57ecU4ThqCQBw2RTw448/LhMnTpTk5GRTBDrUlDAih65NWzdzmzyz/ogkHvpjbWdWpSPy3OnbJLXGbketTdMpUi31ouvmNNgLnFb1B4V63klTqeE4agkAcFkdQM0Crly5sqSkpEh0dPFTf++8846EO7fXEdK1abf9fbL8Y+kL5jgwZMo7ejy91w3y9P+NdFRAFW4Ztfqce0z81CR8hPqPVZ9scvVKsnzUuY57zgDgRF6X//62ZQRw8ODB7PnrEqu2ZsnAL94yn3tCrC3QIHDAl/+SVVuvka7NnbUnsAZ5mjQRDjuBhOOoJQDAZQHg7NmzrX5LONSvq1dLg0P7iz2vQWDt3/bJT6tXizTvJ06jAZOTSr38WcCqpV4Kj1omO3TUEgDgsgAQ7lHz0K+W9kPkjFoCAJyNABDHrcVpjeV/x9gP7hu1BAA4V5nvBYzIUbVDO4mO0xVpxeUR+SS6is/0AwAAzkEAiOPm+d+Xktxu79GjwkFg/nFy272mHwAAcA4CQBy/AxkS3+CQ1Ou+V2IqB+/vHBOXa9r1vPYDAADOwRpAHL+qSeaDBnnV6h2S7KyKcuRQtMRUypW4xMPiiQruBwAAnIEA0IHyjhyR3Z9/Lgf37JcqtapLne7dJSrGgd+qht1E4uuKeHeLJ8onVZIOF+rgyT+v/QAAgGM4MKpwt23vfyjLFh6Sg0dq6NCZ7gMhVd76t/TsXUma9HdYLb2oaJG+E0XmDT5akjhEieK+E/L7AQAAx2ANoMOCv9QFleTgkYSg9oNHqpt2Pe84rfqLDJgjEl+oELGO/Gm7ngcAAI7CCKCDpn115E+kUrEbqy1f+Js0vuCI86aDNchr2U9kx4r8hA9d86fTvoz8AQDgSA6LJNzLrPkz077FiZIDR2qafvXOPlscR4O9xj3L+y4AAMAxYArYITThw8p+AAAAxSEAdAjN9rWyHwAAQHEIAB1CS71UidFdNYILKv8hT6rG/GL6AQAAuCYAnD59ujRq1EgqVaokXbp0kVWrVpXY/6233pKWLVua/m3atJEFCxYEnff5fHL//fdLnTp1pHLlypKSkiJbtmyR8qCJHVrqJT8BpHAQqMce6dG7svMSQAAAQNgJmwBw7ty5MnLkSBk3bpykpaXJGWecIX369JHMzMyQ/VesWCFXXXWVDB06VNauXSuXXHKJeW3YsKGgz+OPPy5PP/20zJgxQ7788kupUqWKec9DhzQbt+xpnb++FxySKjHB6/yqxuwz7Y6rAwgAAMKSx6fDYGFAR/w6deok06ZNM8d5eXnSoEEDGT58uIwePbpI/4EDB8rBgwdl/vz5BW1nnnmmtG3b1gR8+seuW7eu3HnnnXLXXXeZ8/v375ekpCSZPXu2XHnllX96T16vV6pXr26+Lj4+3rI/a25Ojmx/7S05kL5PqiYnSOOrr5Do2FjL3h8AADfz2vT7O5yExQjg4cOH5auvvjJTtH5RUVHmeOXKlSG/RtsD+ysd3fP33759u6Snpwf10R8GDTSLe8+y4F24UL7v01d+f/wRiZ0z3XzUY20HAABwTQC4Z88eyc3NNaNzgfRYg7hQtL2k/v6PpXnPnJwc86+GwJeVNMjbefsIOVLo+kcyMkw7QSAAAHBNAOgUjz32mBkl9L90CtoqvtxcyXj0Mc1MCXEyv03Paz8AAICIDwBr1aol0dHRkpGREdSux8nJySG/RttL6u//WJr3HDNmjFkv4H/99NNPYpXsNV8VGfkL4vOZ89oPAAAg4gPAihUrSocOHWTx4sUFbZoEosddu3YN+TXaHthfffLJJwX9GzdubAK9wD46pavZwMW9Z2xsrFksGviyypGsLEv7AQAAFCdsisppCZghQ4ZIx44dpXPnzjJlyhST5XvdddeZ84MHD5Z69eqZaVp1++23y9lnny2TJk2Sfv36yZtvvilr1qyR559/3pz3eDwyYsQIGT9+vDRr1swEhPfdd5/JDNZyMWUtJjHR0n4AAABhHwBqWZesrCxTuFmTNLScS2pqakESx48//mgyg/26desmr7/+utx7770yduxYE+S999570rp164I+//znP00QedNNN8m+ffukR48e5j21cHRZi23fXn6uUkVqHDwYclhWS0HvrVJFmrVvX+b3BgAAIkvY1AGM9DpCn2/NlFnP95O73j8g+g2JKrIPiMiT/avK0Js+lO5Na5/wvQMA4FZe6gCGxxpAN1iVvkZWn3ZIJl0aJb9UCz6nx9qu57UfAACAK6aAI11UzAHzcVWLKFndzCOn/uSTGgdE9lYV+baBR3xRnqB+AAAAx4sA0CG6nNxIXtiU/7kGexsbeortBwAAcCKYAnaITskdpHqFWiHrQCttT6iQaPoBAACcCAJAh4iOipYHut8jHh34KxwE+rRsjci47mNNPwAAgBNBAOggKQ1T5KleT0lSlUL7E1dJNu16HgAA4ESxBtBhNMg7p8E5kpaZJlnZWZIYlyjta7dn5A8AAFiGANCBdJq3U3Kn8r4NAAAQoZgCBgAAcBkCQAAAAJchAAQAAHAZAkAAAACXIQAEAABwGQJAAAAAlyEABAAAcBkCQAAAAJchAAQAAHAZdgI5AT6fz3z0er1WfT8AAIDNvEd/b/t/j7sRAeAJ+PXXX83HBg0aWPX9AAAAZfh7vHr16q583h6fm8PfE5SXlye7du2SatWqicfjsfxfJxpY/vTTTxIfH2/pe4PnXNb4eeY5RxJ+nsP/Oft8PhP81a1bV6Ki3LkajhHAE6A/NPXr1xc76Q89AaD9eM5lg+fMc44k/DyH93Ou7tKRPz93hr0AAAAuRgAIAADgMgSADhUbGyvjxo0zH8FzDnf8PPOcIwk/zzznSEASCAAAgMswAggAAOAyBIAAAAAuQwAIAADgMgSAAAAALkMAWEamT58ujRo1kkqVKkmXLl1k1apVJfZ/6623pGXLlqZ/mzZtZMGCBUWqmN9///1Sp04dqVy5sqSkpMiWLVvE7ax8zr///ruMGjXKtFepUsVUjB88eLDZ/cXtrP55DjRs2DCzs86UKVNsuPPwY8ez/vbbb6V///6mEK7+bHfq1El+/PFHcTOrn/OBAwfk1ltvNZsF6N/RrVq1khkzZojbleY5f/PNN3LZZZeZ/iX9nVDa7x2O0q3gYK8333zTV7FiRd+LL77o++abb3w33nijLyEhwZeRkRGy/+eff+6Ljo72Pf74476NGzf67r33Xl+FChV8//3vfwv6TJgwwVe9enXfe++95/v66699/fv39zVu3Nj322+/ufbbafVz3rdvny8lJcU3d+5c33fffedbuXKlr3Pnzr4OHTr43MyOn2e/d955x3fGGWf46tat63vqqad8bmfHs966dauvZs2avrvvvtuXlpZmjv/9738X+55uYMdz1vdo0qSJb8mSJb7t27f7nnvuOfM1+qzdqrTPedWqVb677rrL98Ybb/iSk5ND/p1Q2vfEHwgAy4AGDf/4xz8KjnNzc80vuMceeyxk/wEDBvj69esX1NalSxffzTffbD7Py8sz/zE88cQTBec1WImNjTX/obiV1c+5uL+Q9N9NO3bs8LmVXc/5f//7n69evXq+DRs2+Bo2bEgAaNOzHjhwoG/QoEGl+6ZHODue82mnneZ76KGHgvq0b9/ed8899/jcqrTPOVBxfyecyHu6HVPANjt8+LB89dVXZoo2cA9hPV65cmXIr9H2wP6qT58+Bf23b98u6enpQX10KkeHvot7z0hnx3MOZf/+/WYqIiEhQdzIruecl5cn11xzjdx9991y2mmn2fgncPez1uf84YcfSvPmzU177dq1zd8b7733nriVXT/T3bp1k/fff1927txpluwsWbJENm/eLL179xY3Op7nXB7v6SYEgDbbs2eP5ObmSlJSUlC7HmsQF4q2l9Tf/7E07xnp7HjOhR06dMisCbzqqqts2Zjczc954sSJEhMTI7fddptNdx5+7HjWmZmZZm3ahAkTpG/fvrJw4UL561//Kpdeeql89tln4kZ2/Uw/88wzZt2frgGsWLGied66Vu2ss84SNzqe51we7+kmMeV9A0A40ISQAQMGmH/JP/vss+V9OxFF/wU/depUSUtLM6OrsI+OAKqLL75Y7rjjDvN527ZtZcWKFSZB4eyzz+bxW0QDwC+++MKMAjZs2FD+85//yD/+8Q+TTFZ49BAoD4wA2qxWrVoSHR0tGRkZQe16nJycHPJrtL2k/v6PpXnPSGfHcy4c/O3YsUM++eQT147+2fWcly1bZkamTj75ZDMKqC991nfeeafJ7HMrO561vqc+Xx2ZCnTqqae6NgvYjuf822+/ydixY2Xy5Mly0UUXyemnn24yggcOHChPPvmkuNHxPOfyeE83IQC0mQ79d+jQQRYvXhz0r3A97tq1a8iv0fbA/koDD3//xo0bmx/uwD5er1e+/PLLYt8z0tnxnAODPy2xs2jRIjnppJPEzex4zrr2b/369bJu3bqCl46S6HrAjz/+WNzKjmet76klXzZt2hTUR9em6SiVG9nxnPXvDX3perRAGqz4R2Hd5niec3m8p6uUdxaKG2iaumbozp4925QMuOmmm0yaenp6ujl/zTXX+EaPHh1UYiAmJsb35JNP+r799lvfuHHjQpaB0ffQkgLr16/3XXzxxZSBsfg5Hz582JTXqV+/vm/dunW+3bt3F7xycnJ8bmXHz3NhZAHb96y11I62Pf/8874tW7b4nnnmGVOeZNmyZT63suM5n3322SYTWMvAfP/9976XXnrJV6lSJd///d//+dyqtM9Z/55du3atedWpU8eUhNHP9ef2WN8TxSMALCP6l+zJJ59s6hVp2voXX3wR9BfFkCFDgvrPmzfP17x5c9Nf/xL58MMPg85rKZj77rvPl5SUZH74zzvvPN+mTZt8bmflc9baXfpvpFAv/Uvdzaz+eS6MANDeZz1r1ixf06ZNTUCidRe1nqjbWf2c9R+K1157rSlJos+5RYsWvkmTJpm/u92sNM+5uL+Dtd+xvieK59H/K+9RSAAAAJQd1gACAAC4DAEgAACAyxAAAgAAuAwBIAAAgMsQAAIAALgMASAAAIDLEAACAAC4DAEgAACAyxAAAggL1157rVxyySXldn3ds/jRRx89ofeYPXu2JCQklOprrrzySpk0adIJXRcACmMnEADlzuPxlHh+3Lhxcscdd+jWlaUOoKzw9ddfy7nnnis7duyQqlWrHvf7/Pbbb/Lrr79K7dq1j/lrNmzYIGeddZZs375dqlevftzXBoBABIAAyl16enrB53PnzpX7779fNm3aVNCmQdeJBF4n6oYbbpCYmBiZMWNGuVy/U6dOZgT0H//4R7lcH0DkYQoYQLlLTk4ueOkol44IBrZp8Fd4CrhXr14yfPhwGTFihNSoUUOSkpJk5syZcvDgQbnuuuukWrVq0rRpU/noo4+KjKj95S9/Me+pX6NTu3v27Cn23nJzc+Vf//qXXHTRRUHtjRo1kvHjx8vgwYPNezVs2FDef/99ycrKkosvvti0nX766bJmzZpip4AfeOABadu2rbzyyivm/fTPrlO+OkoYSK/95ptvntAzBoBABIAAwtbLL78stWrVklWrVplg8JZbbpErrrhCunXrJmlpadK7d28T4GVnZ5v++/btM1O57dq1M4FZamqqZGRkyIABA4q9xvr162X//v3SsWPHIueeeuop6d69u6xdu1b69etnrqUB4aBBg8z1mzRpYo516ro427Ztk/fee0/mz59vXp999plMmDAhqE/nzp3NnzEnJ+eEnhcA+BEAAghbZ5xxhtx7773SrFkzGTNmjFSqVMkEhDfeeKNp06nkn3/+2QRxatq0aSb402SOli1bms9ffPFFWbJkiWzevDnkNXTdX3R0dMh1exdccIHcfPPNBdfyer1mulaD0ObNm8uoUaPk22+/NUFmcfLy8szIYOvWraVnz54miFy8eHFQn7p168rhw4eDpsoB4ETEnNBXA0A50ilWPw3STjrpJGnTpk1Bm07xqszMzIJkDg32Qq0n1JE4DdpCJW7ExsaGTFQJvL7/WsVdX6eyQ9GpX52u9qtTp07B/fpVrlzZfPSPZALAiSIABBC2KlSoEHSsQVpgmz9o01E2deDAAbOebuLEiUXeSwOvUHREUQMvHYGrWLFisdf3X6uk6x/rn6Fw/19++cV8TExMLPZ9AKA0CAABuEb79u3l7bffNqNumtV7LDRJQ23cuLHg87KmiSv169c3wSgAWIE1gABcQ8uo6GjaVVddJatXrzbTvh9//LHJGtZs31B01E0Dx+XLl0t5WbZsmUloAQCrEAACcA1Npvj8889NsKcBla7X0zIyWpolKiqqxDqAr732mpSHQ4cOmSxhTWwBAKtQCBoA/oQmgrRo0cIUqe7atWuZPq9nn31W3n33XVm4cGGZXhdAZGMEEAD+hGbhzpkzp8SC0XbRJJFnnnmmzK8LILIxAggAAOAyjAACAAC4DAEgAACAyxAAAgAAuAwBIAAAgMsQAAIAALgMASAAAIDLEAACAAC4DAEgAACAyxAAAgAAiLv8P9ubM62mXl/FAAAAAElFTkSuQmCC", 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\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.plot(A0_5.Time, , label='h=1')\n", "#ax.scatter(A0B0.Time, A0B0.NADPH, label='A=0')\n", "ax.scatter(A0_5.Time, A0_5.NADPH, label='A=0.5')\n", "ax.scatter(A1.Time, A1.NADPH, label='A=1')\n", "ax.scatter(A2.Time, A2.NADPH, label='A=2')\n", "ax.scatter(A4.Time, A4.NADPH, label='A=4')\n", "ax.scatter(A8.Time, A8.NADPH, label='A=8')\n", "ax.set_xlabel('Time (min)')\n", "ax.set_ylabel('NADPH Concentration (mM), Changing A')\n", "ax.legend()" ] }, { "cell_type": "code", "execution_count": 55, "id": "3bc44182-cb31-4727-8441-dcb91b40f954", "metadata": {}, "outputs": [], "source": [ "#Plotting B" ] }, { "cell_type": "code", "execution_count": 56, "id": "05f78333-bb32-4fd2-bd54-59ccde167af8", "metadata": {}, "outputs": [], "source": [ "#A0B0 = pd.read_csv('A0B0.csv', names=['Time', 'NADPH'])\n", "B1_5 = pd.read_csv('A8B1.5.csv', names=['Time', 'NADPH'])\n", "B3 = pd.read_csv('A8B3.csv', names=['Time', 'NADPH'])\n", "B6 = pd.read_csv('A8B6.csv', names=['Time', 'NADPH'])\n", "B12 = pd.read_csv('A8B12.csv', names=['Time', 'NADPH'])\n", "B24 = pd.read_csv('A8B24.csv', names=['Time', 'NADPH'])" ] }, { "cell_type": "code", "execution_count": 58, "id": "ed589141-4959-44eb-97dd-f65c98aaa1b4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ad18ed123d32416fba131c9a7fe2ed5c", "version_major": 2, "version_minor": 0 }, "image/png": 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", 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\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(A0B0.Time, A0B0.NADPH, label='B=0')\n", "ax.scatter(B1_5.Time, B1_5.NADPH, label='B=1.5')\n", "ax.scatter(B3.Time, B3.NADPH, label='B=3')\n", "ax.scatter(B6.Time, B6.NADPH, label='B=6')\n", "ax.scatter(B12.Time, B12.NADPH, label='B=12')\n", "ax.scatter(B24.Time, B24.NADPH, label='B=24')\n", "ax.set_xlabel('Time (min)')\n", "ax.set_ylabel('NADPH Concentration (mM) Changing B')\n", "ax.legend()" ] }, { "cell_type": "code", "execution_count": 59, "id": "eb98a0a7-f730-4bea-940d-5bb920ef874b", "metadata": {}, "outputs": [], "source": [ "#RegressionsA" ] }, { "cell_type": "code", "execution_count": 60, "id": "b0a132ed-ff74-4277-98f4-e3df01b8b5b8", "metadata": {}, "outputs": [], "source": [ "regA0_5 = sp.stats.linregress(A0_5.Time, A0_5.NADPH)\n", "regA1 = sp.stats.linregress(A1.Time, A1.NADPH)\n", "regA2 = sp.stats.linregress(A2.Time, A2.NADPH)\n", "regA4 = sp.stats.linregress(A4.Time, A4.NADPH)\n", "regA8 = sp.stats.linregress(A8.Time, A8.NADPH)\n", "regB1_5 = sp.stats.linregress(B1_5.Time, B1_5.NADPH)\n", "regB3 = sp.stats.linregress(B3.Time, B3.NADPH)\n", "regB6 = sp.stats.linregress(B6.Time, B6.NADPH)\n", "regB12 = sp.stats.linregress(B12.Time, B12.NADPH)\n", "regB24 = sp.stats.linregress(B24.Time, B24.NADPH)" ] }, { "cell_type": "code", "execution_count": 61, "id": "17872b27-44bd-4f23-969d-76f4c4b62c27", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.24118661159479945\n", "0.35263044490832535\n", "0.5746500696105541\n", "0.7322856639928835\n", "0.8250788434733142\n", "0.25422083243133853\n", "0.3679535702854621\n", "0.5564088293747241\n", "0.701203980466376\n", "0.8250788434733142\n" ] } ], "source": [ "regressionsAB = [regA0_5, regA1, regA2, regA4, regA8, regB1_5, regB3, regB6, regB12, regB24]\n", "rates = []\n", "for reg in regressionsAB:\n", " print (reg.slope)\n", " rates.append(reg.slope)\n", "rates = np.array(rates)" ] }, { "cell_type": "code", "execution_count": 62, "id": "9f9baec7-50bd-4dc3-98e7-7309475f28fd", "metadata": {}, "outputs": [], "source": [ "#RegressionsB" ] }, { "cell_type": "code", "execution_count": 63, "id": "49e49a98-ac7a-47a6-9565-8bdfae9c7091", "metadata": {}, "outputs": [], "source": [ "#regB0 = sp.stats.linregress(B0.Time, B0.NADPH)\n", "#regB1_5 = sp.stats.linregress(B1_5.Time, B1_5.NADPH)\n", "#regB3 = sp.stats.linregress(B3.Time, B3.NADPH)\n", "#regB6 = sp.stats.linregress(B6.Time, B6.NADPH)\n", "#regB12 = sp.stats.linregress(B12.Time, B12.NADPH)\n", "#regB24 = sp.stats.linregress(B24.Time, B24.NADPH)" ] }, { "cell_type": "code", "execution_count": 64, "id": "bf0eada5-6a80-4e07-acab-4c2dbde87269", "metadata": {}, "outputs": [], "source": [ "#regressionsB = [regB0, regB1_5, regB3, regB6, regB12, regB24]\n", "#ratesB = []\n", "#for reg in regressionsB:\n", " #print (reg.slope)\n", " #ratesB.append(reg.slope)\n", "#ratesA = np.array(ratesB)" ] }, { "cell_type": "code", "execution_count": 65, "id": "6068dd0e-4832-4993-a8fe-f3d850084d83", "metadata": {}, "outputs": [], "source": [ "#Combined Dataframe" ] }, { "cell_type": "code", "execution_count": 66, "id": "e9dfb552-41c7-46c8-8453-d0bda8351a51", "metadata": {}, "outputs": [], "source": [ "concsA = np.array([0, 0.5, 1, 2, 4, 8, 8, 8, 8, 8])\n", "concsB = np.array([0, 24, 24, 24, 24, 24, 1.5, 3, 6, 12])\n", "comb = pd.DataFrame({'A [mM]': concsA, 'B [mM]': concsB, 'Rate (mM/min-1)': rates})" ] }, { "cell_type": "code", "execution_count": 67, "id": "9489d64e-b05e-4ed0-bf00-6c5fa2a5f54d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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A [mM]B [mM]Rate (mM/min-1)
00.00.00.241187
10.524.00.352630
21.024.00.574650
32.024.00.732286
44.024.00.825079
58.024.00.254221
68.01.50.367954
78.03.00.556409
88.06.00.701204
98.012.00.825079
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" ], "text/plain": [ " A [mM] B [mM] Rate (mM/min-1)\n", "0 0.0 0.0 0.241187\n", "1 0.5 24.0 0.352630\n", "2 1.0 24.0 0.574650\n", "3 2.0 24.0 0.732286\n", "4 4.0 24.0 0.825079\n", "5 8.0 24.0 0.254221\n", "6 8.0 1.5 0.367954\n", "7 8.0 3.0 0.556409\n", "8 8.0 6.0 0.701204\n", "9 8.0 12.0 0.825079" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "comb" ] }, { "cell_type": "code", "execution_count": 68, "id": "0df33407-05fb-4af0-988b-f12e04f5a10e", "metadata": {}, "outputs": [], "source": [ "def v(Vf,A,B,Ka,Kb):\n", " return (Vf*A*B)/((Ka+A)*(Kb+B))" ] }, { "cell_type": "code", "execution_count": 69, "id": "978d2a40-5d26-4a99-a4f6-38e824294196", "metadata": {}, "outputs": [], "source": [ "from lmfit import Model\n", "mymod = Model(v, independent_vars=['A', 'B'])\n", "mypar = mymod.make_params(Vf=1, Ka=1, Kb=1)" ] }, { "cell_type": "code", "execution_count": 70, "id": "5043108e-fe73-478e-84b2-6763e60a2524", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Parameters
namevalueinitial valueminmaxvary
Vf 1.000000001.0 -inf infTrue
Ka 1.000000001.0 -inf infTrue
Kb 1.000000001.0 -inf infTrue
" ], "text/plain": [ "Parameters([('Vf', ), ('Ka', ), ('Kb', )])" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mypar" ] }, { "cell_type": "code", "execution_count": 71, "id": "04a94696-7d23-444c-a6e5-07a77fe8c59d", "metadata": {}, "outputs": [], "source": [ "myfit = mymod.fit(rates, mypar, A=concsA, B=concsB)" ] }, { "cell_type": "code", "execution_count": 72, "id": "edda1b22-9ca7-4797-99a7-f488e3db42ce", "metadata": {}, "outputs": [ { "data": { "text/html": [ "

Fit Result

Model: Model(v)

Fit Statistics
fitting methodleastsq
# function evals38
# data points10
# variables3
chi-square 0.33248017
reduced chi-square 0.04749717
Akaike info crit.-28.0376015
Bayesian info crit.-27.1298462
R-squared 0.28119006
Parameters
namevaluestandard errorrelative errorinitial valueminmaxvary
Vf 0.73875320 0.18373907(24.87%)1.0 -inf infTrue
Ka 0.30326117 0.42218832(139.22%)1.0 -inf infTrue
Kb 0.94078589 1.29155946(137.29%)1.0 -inf infTrue
Correlations (unreported values are < 0.100)
Parameter1Parameter 2Correlation
VfKb+0.7144
VfKa+0.7130
KaKb+0.3573
" ], "text/plain": [ "" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "myfit" ] }, { "cell_type": "code", "execution_count": null, "id": "fd74594a-2f0d-4af3-93d2-81e4dbeab783", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.2" } }, "nbformat": 4, "nbformat_minor": 5 }