{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "66a835c4-1cce-47be-87f8-f171e7f27629", "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": 2, "id": "7a6260e1-7a96-42ee-9413-7bdd148363a0", "metadata": {}, "outputs": [], "source": [ "A05 = pd.read_csv('A0.5B24.csv', delimiter=',', names=['Time', 'NADHP'])\n", "A1 = pd.read_csv('A1B24.csv', delimiter=',', names=['Time', 'NADHP'])\n", "A2 = pd.read_csv('A2B24.csv', delimiter=',', names=['Time', 'NADHP'])\n", "A4 = pd.read_csv('A4B24.csv', delimiter=',', names=['Time', 'NADHP'])\n", "A8 = pd.read_csv('A8B24.csv', delimiter=',', names=['Time', 'NADHP'])\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "255fbd83-606b-4f19-9dee-0031929416d0", "metadata": {}, "outputs": [], "source": [ "df1 = pd.DataFrame(A05)\n", "df2 = pd.DataFrame(A1)\n", "df3 = pd.DataFrame(A2)\n", "df4 = pd.DataFrame(A4)\n", "df5 = pd.DataFrame(A8)" ] }, { "cell_type": "code", "execution_count": 4, "id": "a109be1d-8a37-4e20-a54c-3dfaaf5573e4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "867b8afecddb44329ce0c7d6a3e95dc3", "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": [ "ax = df1.plot.scatter(x=\"Time\", y=\"NADHP\", color=\"DarkBlue\", label=\"A 0.5 Mm\")\n", "ax= df2.plot.scatter(x=\"Time\", y=\"NADHP\", color=\"DarkGreen\", label=\"A 1 Mm\", ax=ax)\n", "ax= df3.plot.scatter(x=\"Time\", y=\"NADHP\", color=\"Red\", label=\"A 2 Mm\", ax=ax)\n", "ax= df4.plot.scatter(x=\"Time\", y=\"NADHP\", color=\"Yellow\", label=\"A 4 Mm\", ax=ax)\n", "ax= df5.plot.scatter(x=\"Time\", y=\"NADHP\", color=\"Orange\", label=\"A 8 Mm\",ax=ax)\n", "ax.legend()" ] }, { "cell_type": "code", "execution_count": 5, "id": "74a19544-4deb-4c06-9a71-bc85f0ba0afd", "metadata": {}, "outputs": [], "source": [ "B1_5 = pd.read_csv('A8B1.5.csv', delimiter=',', names=['Time', 'NADHP'])\n", "B3 = pd.read_csv('A8B3.csv', delimiter=',', names=['Time', 'NADHP'])\n", "B6 = pd.read_csv('A8B6.csv', delimiter=',', names=['Time', 'NADHP'])\n", "B12 = pd.read_csv('A8B12.csv', delimiter=',', names=['Time', 'NADHP'])\n", "B24 = pd.read_csv('A8B24.csv', delimiter=',', names=['Time', 'NADHP'])\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "ff4c8063-0a2b-4b51-ac7e-2a080749fc1a", "metadata": {}, "outputs": [], "source": [ "df1 = pd.DataFrame(B1_5)\n", "df2 = pd.DataFrame(B3)\n", "df3 = pd.DataFrame(B6)\n", "df4 = pd.DataFrame(B12)\n", "df5 = pd.DataFrame(B24)" ] }, { "cell_type": "code", "execution_count": 8, "id": "d4354661-b609-4b22-bfde-5f6fc64a62c6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "41ef72c61cfe4de9b19275b7d2459d65", "version_major": 2, "version_minor": 0 }, "image/png": 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RateAB
00.2542210.50.0
10.3679541.00.0
20.5564092.00.0
30.7012044.00.0
40.8250798.00.0
00.2542210.01.5
10.3679540.03.0
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40.8250790.024.0
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" ], "text/plain": [ " Rate A B\n", "0 0.254221 0.5 0.0\n", "1 0.367954 1.0 0.0\n", "2 0.556409 2.0 0.0\n", "3 0.701204 4.0 0.0\n", "4 0.825079 8.0 0.0\n", "0 0.254221 0.0 1.5\n", "1 0.367954 0.0 3.0\n", "2 0.556409 0.0 6.0\n", "3 0.701204 0.0 12.0\n", "4 0.825079 0.0 24.0" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "All_Data" ] }, { "cell_type": "code", "execution_count": 74, "id": "52881e4c-1caa-4ffa-a78a-d22c5867d973", "metadata": {}, "outputs": [], "source": [ "def vf (A, Ka):\n", " return A*Ka\n", "\n" ] }, { "cell_type": "code", "execution_count": 75, "id": "b06ceb3b-2a3b-4150-9ec5-640b86744bba", "metadata": {}, "outputs": [], "source": [ "from lmfit import Model\n", "mymod = Model(vf)\n", "mypar = mymod.make_params(Ka=1)" ] }, { "cell_type": "code", "execution_count": 78, "id": "c32fc838-565d-46d2-819c-d4e3d7a75945", "metadata": {}, "outputs": [], "source": [ "myfit = mymod.fit(dataA.Rate, mypar, A=dataA.A)" ] }, { "cell_type": "code", "execution_count": 79, "id": "d3c9fa1e-bd5c-4aca-8c0b-7b394102d933", "metadata": {}, "outputs": [ { "data": { "text/html": [ "

Fit Result

Model: Model(vf)

Fit Statistics
fitting methodleastsq
# function evals5
# data points5
# variables1
chi-square 0.25925448
reduced chi-square 0.06481362
Akaike info crit.-12.7969153
Bayesian info crit.-13.1874774
R-squared-0.18494183
Parameters
namevaluestandard errorrelative errorinitial valueminmaxvary
Ka 0.12918860 0.02757312(21.34%)1.0 -inf infTrue
" ], "text/plain": [ "" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "myfit" ] }, { "cell_type": "code", "execution_count": 82, "id": "de423b1a-5a32-4b63-872b-6a22402f5bc1", "metadata": {}, "outputs": [], "source": [ "def Vr(B, Kb):\n", " return -B*Kb\n", "\n" ] }, { "cell_type": "code", "execution_count": 83, "id": "044ba934-45f1-4423-9c18-970c74f0da1a", "metadata": {}, "outputs": [], "source": [ "from lmfit import Model\n", "mymod = Model(Vr)\n", "mypar = mymod.make_params(Kb=1)" ] }, { "cell_type": "code", "execution_count": 84, "id": "5f58ba61-f44f-42c4-8828-175865c1579d", "metadata": {}, "outputs": [], "source": [ "myfit = mymod.fit(dataB.Rate, mypar, B=dataB.B)" ] }, { "cell_type": "code", "execution_count": 85, "id": "24ce9da5-2e38-4857-a302-ed0e17aad9d4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "

Fit Result

Model: Model(Vr)

Fit Statistics
fitting methodleastsq
# function evals5
# data points5
# variables1
chi-square 0.25925448
reduced chi-square 0.06481362
Akaike info crit.-12.7969153
Bayesian info crit.-13.1874774
R-squared-0.18494183
Parameters
namevaluestandard errorrelative errorinitial valueminmaxvary
Kb-0.04306287 0.00919104(21.34%)1.0 -inf infTrue
" ], "text/plain": [ "" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "myfit" ] }, { "cell_type": "code", "execution_count": null, "id": "de8cc41c-655f-4c22-95e1-81721c55bb36", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 72, "id": "65f090e7-a516-43d6-87b5-90b2926f8a9a", "metadata": {}, "outputs": [], "source": [ "def v(A, B):\n", " return (Vf*A*B)/(Ka+A)*(Kb+B)" ] }, { "cell_type": "code", "execution_count": null, "id": "228b657a-bb69-4116-a888-afcb45ab368c", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 73, "id": "8525cacc-ffde-47a3-be50-226d4a6c4873", "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", "\u001b[32m~\\AppData\\Local\\Temp\\ipykernel_21236\\2060707855.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m lmfit \u001b[38;5;28;01mimport\u001b[39;00m Model\n\u001b[32m 2\u001b[39m \n\u001b[32m 3\u001b[39m mymod = Model(v)\n\u001b[32m 4\u001b[39m mypar = mymod.make_params(Ka=\u001b[32m1\u001b[39m,Kb=\u001b[32m1\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m myfit = mymod.fit(All_Data.Rate, mypar, A=dataA.A, B=dataB.B)\n\u001b[32m 6\u001b[39m myfit\n", "\u001b[32m~\\miniconda3\\envs\\minicourse\\Lib\\site-packages\\lmfit\\model.py\u001b[39m in \u001b[36m?\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 1117\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m isinstance(p, Parameter):\n\u001b[32m 1118\u001b[39m p.name = name \u001b[38;5;66;03m# allows N=Parameter(value=5) with implicit name\u001b[39;00m\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 params[name].set(value=p)\n\u001b[32m 1122\u001b[39m \u001b[38;5;28;01mdel\u001b[39;00m kwargs[name]\n\u001b[32m 1123\u001b[39m \n\u001b[32m 1124\u001b[39m \u001b[38;5;66;03m# All remaining kwargs should correspond to independent variables.\u001b[39;00m\n", "\u001b[32m~\\miniconda3\\envs\\minicourse\\Lib\\site-packages\\lmfit\\parameter.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m(self, value, vary, min, max, expr, brute_step, is_init_value)\u001b[39m\n\u001b[32m 859\u001b[39m \u001b[38;5;66;03m# need to set this after min and max, so that it will use new\u001b[39;00m\n\u001b[32m 860\u001b[39m \u001b[38;5;66;03m# bounds in the setter for value\u001b[39;00m\n\u001b[32m 861\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m value \u001b[38;5;28;01mis\u001b[39;00m \u001b[38;5;28;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;28;01mor\u001b[39;00m self.value \u001b[38;5;28;01min\u001b[39;00m (\u001b[38;5;28;01mNone\u001b[39;00m, -inf, inf)\n\u001b[32m--> \u001b[39m\u001b[32m863\u001b[39m self.value = value\n\u001b[32m 864\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_init_value:\n\u001b[32m 865\u001b[39m self.init_value = value\n\u001b[32m 866\u001b[39m self.__set_expression(\u001b[33m\"\"\u001b[39m)\n", "\u001b[32m~\\miniconda3\\envs\\minicourse\\Lib\\site-packages\\lmfit\\parameter.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m(self, val)\u001b[39m\n\u001b[32m 1016\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m value(self, val):\n\u001b[32m 1017\u001b[39m \u001b[33m\"\"\"Set the numerical Parameter value.\"\"\"\u001b[39m\n\u001b[32m 1018\u001b[39m self._val = val\n\u001b[32m 1019\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m self._val \u001b[38;5;28;01mis\u001b[39;00m \u001b[38;5;28;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 self._val > self.max:\n\u001b[32m 1021\u001b[39m self._val = self.max\n\u001b[32m 1022\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m self._val < self.min:\n\u001b[32m 1023\u001b[39m self._val = self.min\n", "\u001b[32m~\\miniconda3\\envs\\minicourse\\Lib\\site-packages\\pandas\\core\\generic.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 1511\u001b[39m @final\n\u001b[32m 1512\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m __bool__(self) -> NoReturn:\n\u001b[32m-> \u001b[39m\u001b[32m1513\u001b[39m raise ValueError(\n\u001b[32m 1514\u001b[39m f\"The truth value of a {type(self).__name__} is ambiguous. \"\n\u001b[32m 1515\u001b[39m \u001b[33m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[39m\n\u001b[32m 1516\u001b[39m )\n", "\u001b[31mValueError\u001b[39m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()." ] } ], "source": [ "from lmfit import Model\n", "\n", "mymod = Model(v)\n", "mypar = mymod.make_params(Ka=1,Kb=1)\n", "myfit = mymod.fit(All_Data.Rate, mypar, A=dataA.A, B=dataB.B)\n", "myfit" ] }, { "cell_type": "code", "execution_count": 57, "id": "3011a0e1-2a6d-4263-a977-0b7cbde23aef", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Parameters
namevalueinitial valueminmaxvary
B -infNone -inf infTrue
" ], "text/plain": [ "Parameters([('B', )])" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mypar" ] }, { "cell_type": "code", "execution_count": null, "id": "711d2d5e-bbc2-4ae2-9240-154058689d0a", "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": "3.13.12" } }, "nbformat": 4, "nbformat_minor": 5 }