{ "cells": [ { "cell_type": "markdown", "id": "6a63bd76", "metadata": {}, "source": [ "This example shows a basic example of \"Feff fitting\" with larch. \n", "\n", "We start with some import statements, then \n", " 1. read in data for ZnSe, Zn K edge\n", " 2. do background subtraction and plot $\\chi(k)$\n", " 3. set the Fourier transform ranges\n", " 4. define a Group of Parameters for the fit\n", " 5. read in a Feff Path for Zn-Se \n", " 6. run the fit and view the results" ] }, { "cell_type": "code", "execution_count": 3, "id": "59499dc6", "metadata": {}, "outputs": [], "source": [ "# step 0: imports\n", "import numpy as np\n", "from larch.xafs import (pre_edge, autobk, xftf, \n", " ff2chi, feffpath, feffit_transform, feffit_dataset, feffit, feffit_report)\n", "from larch.fitting import param, guess, param_group\n", "from larch.io import read_ascii\n", "from larch.plot.plotly_xafsplots import (plot_mu, plot_chik, plot_chir, plot_chifit, \n", " multi_plot, plotlabels, set_label_weight)" ] }, { "cell_type": "code", "execution_count": 4, "id": "c431a710", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "line": { "color": "#1f77b4", "width": 3 }, "name": "mu", "type": "scatter", "uid": "0069856f-ff71-4e96-9803-068e80365187", "x": [ 9509, 9514, 9519, 9524, 9529, 9534, 9539, 9544, 9549, 9554, 9559, 9564, 9569, 9574, 9579, 9584, 9589, 9594, 9599, 9604, 9609, 9614, 9619, 9624, 9629, 9634, 9639, 9644, 9649, 9649.25, 9649.5, 9649.75, 9650, 9650.25, 9650.5, 9650.75, 9651, 9651.25, 9651.5, 9651.75, 9652, 9652.25, 9652.5, 9652.75, 9653, 9653.25, 9653.5, 9653.75, 9654, 9654.25, 9654.5, 9654.75, 9655, 9655.25, 9655.5, 9655.75, 9656, 9656.25, 9656.5, 9656.75, 9657, 9657.25, 9657.5, 9657.75, 9658, 9658.25, 9658.5, 9658.75, 9659, 9659.25, 9659.5, 9659.75, 9660, 9660.25, 9660.5, 9660.75, 9661, 9661.25, 9661.5, 9661.75, 9662, 9662.25, 9662.5, 9662.75, 9663, 9663.25, 9663.5, 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"znse_zn_xafs.001" }, "width": 650, "xaxis": { "color": "#004", "gridcolor": "#D8D8D8", "showgrid": true, "title": { "text": "$R \\rm\\,(\\unicode{x212B})$" }, "zerolinecolor": "#DDD" }, "yaxis": { "color": "#004", "gridcolor": "#D8D8D8", "showgrid": true, "title": { "text": "$|\\chi(R)| \\rm\\,(\\unicode{x212B}^{{-3}})$" }, "zerolinecolor": "#DDD" } } }, "text/html": [ "
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# step 5: define a Feff Path for Zn-Se scattering\n", "\n", "# using a Path file from a previous Feff calculation for Zn-Se (shown elsewhere)\n", "# here we give math expressions using the Parameters named in the Parameter group \n", "# to set the values for the Path Parameters in the EXAFS equation\n", "path_znse = feffpath('../feffit/feff_znse.dat', \n", " s02='amp', e0='del_e0', sigma2='sig2', deltar='del_r')\n", "\n", "tmpsum = ff2chi([path_znse], params=pars)\n", "fig4 = plot_chik(tmpsum, kweight=2)\n", "fit5 = multi_plot([{'xdata':tmpsum.k, 'ydata':tmpsum.chi*tmpsum.k**2, 'label': 'Feff'},\n", " {'xdata':data.k, 'ydata':data.chi*data.k**2, \n", " 'label': 'Data','xlabel': plotlabels.k, \n", " 'ylabel': set_label_weight(plotlabels.chikw, 2) }])\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "2a63b3c7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=================== FEFFIT RESULTS ====================\n", "[[Statistics]]\n", " n_function_calls = 26\n", " n_variables = 4\n", " n_data_points = 130\n", " n_independent = 12.4591559\n", " chi_square = 246.622347\n", " reduced chi_square = 29.1544865\n", " r-factor = 0.00282983\n", " Akaike info crit = 45.1955944\n", " Bayesian info crit = 47.2854175\n", " \n", "[[Dataset]]\n", " unique_id = 'djwb5nrk'\n", " fit space = 'r'\n", " r-range = 1.000, 3.000\n", " k-range = 3.000, 12.000\n", " k window, dk = 'kaiser', 4.000\n", " paths used in fit = ['../feffit/feff_znse.dat']\n", " k-weight = 2\n", " epsilon_k = Array(mean=4.4808e-4, std=4.4430e-4)\n", " epsilon_r = 0.0063023\n", " n_independent = 12.459\n", " \n", "[[Variables]]\n", " amp = 0.9938163 +/- 0.0547348 (init= 1.0000000)\n", " del_e0 = 5.8934172 +/- 0.6999331 (init= 0.5000000)\n", " del_r = -0.0012320 +/- 0.0034247 (init= 1.0000e-3)\n", " sig2 = 0.0065432 +/- 4.1834e-4 (init= 0.0050000)\n", " \n", "[[Correlations]] (unreported correlations are < 0.100)\n", " amp, sig2 = 0.943\n", " del_e0, del_r = 0.932\n", " amp, del_r = 0.196\n", " del_r, sig2 = 0.191\n", " \n", "[[Paths]]\n", " = Path 'plmkfkjtvz' = Zn K Edge\n", " feffdat file = ../feffit/feff_znse.dat, from feff run 'feffit'\n", " geometry atom x y z ipot\n", " Zn -0.0000, -0.0000, -0.0000 0 (absorber)\n", " Se 1.4169, 1.4169, 1.4169 1\n", " reff = 2.4541000\n", " degen = 4.0000000\n", " n*s02 = 0.9938163 +/- 0.0547348 := 'amp'\n", " e0 = 5.8934172 +/- 0.6999331 := 'del_e0'\n", " r = 2.4528680 +/- 0.0034247 := 'reff + del_r'\n", " deltar = -0.0012320 +/- 0.0034247 := 'del_r'\n", " sigma2 = 0.0065432 +/- 4.1834e-4 := 'sig2'\n", "\n", "=======================================================\n" ] } ], "source": [ "# step 6: run and view the fit\n", "\n", "# now that we have data, a set of paths, and Fourier transform values, we can\n", "# set up a \"Feffit Dataset\" and then fit that with the group of Parameters\n", "\n", "# define dataset to include data, pathlist, transform\n", "dset = feffit_dataset(data=data, pathlist=[path_znse], transform=trans)\n", "\n", "# do the fit...\n", "result = feffit(pars, dset)\n", "\n", "# print out the fit report\n", "print(feffit_report(result))" ] }, { "cell_type": "code", "execution_count": 10, "id": "972296b1", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "line": { "color": "#1f77b4", "width": 3 }, "name": "data", "type": "scatter", "uid": "24f12c04-9841-43c4-9cf8-12870f57669e", "x": [ 0, 0.05, 0.1, 0.15000000000000002, 0.2, 0.25, 0.30000000000000004, 0.35000000000000003, 0.4, 0.45, 0.5, 0.55, 0.6000000000000001, 0.65, 0.7000000000000001, 0.75, 0.8, 0.8500000000000001, 0.9, 0.9500000000000001, 1, 1.05, 1.1, 1.1500000000000001, 1.2000000000000002, 1.25, 1.3, 1.35, 1.4000000000000001, 1.4500000000000002, 1.5, 1.55, 1.6, 1.6500000000000001, 1.7000000000000002, 1.75, 1.8, 1.85, 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# finally, plot the results\n", "fig6, fig7 = plot_chifit(result.datasets[0], show_real=True)" ] }, { "cell_type": "code", "execution_count": null, "id": "9b6f7097", "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": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 5 }