{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 4: Linear combination fitting\n", "\n", "*by Morgane Desmau & Marco Alsina*\n", "\n", "*Last update: June 2021*\n", "\n", "The following notebook explains the following:\n", "\n", "1. Perform linear combination fitting (LCF) on XANES spectra.\n", "2. Perform LCF on EXAFS spectra.\n", "\n", "**Important:** This tutorial assumes you have succesfully completed the previous tutorials in the series:\n", "\n", "- [Part 1: Basics of data processing](01.basics_data_processing.ipynb)\n", "- [Part 2: Normalization and background removal](02.background_removal.ipynb)\n", "- [Part 3: Creating custom report and figures](03.custom_report_figure.ipynb)\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python version : 3.9.4\n", "Numpy version : 1.20.3\n", "Scipy version : 1.6.3\n", "Lmfit version : 1.0.2\n", "H5py version : 3.2.1\n", "Matplotlib version : 3.4.2\n", "Araucaria version : 0.1.10\n" ] } ], "source": [ "# checking version of araucaria and dependencies\n", "from araucaria.utils import get_version\n", "print(get_version(dependencies=True))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Accesing the database\n", "\n", "In this case we will be reading and processing a minerals database measured at the Fe K-edge in the P65 beamline of DESY, Hamburg (data kindly provided by Morgane Desmau):\n", "\n", "1. Fe_database.h5\n", "\n", "We first retrieve the filepath to the database and summarize its contents." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", " \n", " **Note**\n", " \n", " If you prefer to process your own database, just modify the filepath to point to the location of your file.\n", "
" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=================================\n", "id dataset mode n \n", "=================================\n", "1 FeIISO4_20K mu 5 \n", "2 Fe_Foil mu_ref 5 \n", "3 Ferrihydrite_20K mu 5 \n", "4 Goethite_20K mu 5 \n", "=================================\n" ] } ], "source": [ "from pathlib import Path\n", "from araucaria.testdata import get_testpath\n", "from araucaria.io import summary_hdf5\n", "\n", "# retrieving filepath\n", "fpath = get_testpath('Fe_database.h5')\n", "\n", "# summarizing database\n", "report = summary_hdf5(fpath)\n", "report.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Creating a group to fit\n", "\n", "We will use the [merge()](../../xas/xas_merge.rst#araucaria.xas.merge.merge) function to create an example signal to fit, in this case a mixture of the 2 original signals from ferrihydrite and goethite minerals.\n", "\n", "Note that the original signals are multiplied by normalized amplitude factors to prevent proportions larger than 1 during the fit.\n", "\n", "We will store the original signals and the mixture in a [Collection](../../main/main_collection.rst#araucaria.main.collection.Collection), compute normalized spectra through the [apply()](../../main/main_collection.rst#araucaria.main.collection.Collection.apply) method, and produce a summary report." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================================================\n", "id dataset tag mode n edge_step \n", "================================================\n", "1 Ferrihydrite_20K ref mu 5 0.27895 \n", "2 Goethite_20K ref mu 5 0.4038 \n", "3 sample scan mu 2 0.34112 \n", "================================================\n" ] } ], "source": [ "from araucaria.io import read_hdf5\n", "from araucaria.xas import merge, pre_edge, autobk\n", "from araucaria import Collection\n", "\n", "# name of groups\n", "groupnames = ('Ferrihydrite_20K', 'Goethite_20K')\n", "amp = [0.5, 0.5]\n", "\n", "# initializing collections\n", "collection = Collection()\n", "auxiliary = Collection()\n", "\n", "# reading scans and adding them to collection\n", "for i,name in enumerate(groupnames):\n", " group = read_hdf5(fpath, name)\n", " collection.add_group(group, tag='ref')\n", " group.mu = amp[i]*group.mu\n", " auxiliary.add_group(group)\n", "\n", "# merging scans\n", "report, merge = merge(auxiliary, name='sample')\n", "collection.add_group(merge, tag='scan')\n", "\n", "# normalizing spectra in collection\n", "collection.apply(pre_edge)\n", "\n", "# printing report\n", "report = collection.summary(optional=['edge_step'])\n", "report.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. Computing expected amplitudes from the LCF\n", "\n", "As seen from the summary report, the edge steps of the original signals are not equal. We can compute the expected proportions by re-normalizing the amplitudes considering the proportion of the edge step values:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ferrihydrite_20K: 0.409\n", "Goethite_20K : 0.591\n" ] } ], "source": [ "from numpy import sum\n", "\n", "edge_steps = report.get_cols(names=['edge_step'], astype='float')[:2]\n", "amplitudes = [amp[i]*val for i, val in enumerate(edge_steps)]\n", "amplitudes = [val/sum(amplitudes) for val in amplitudes]\n", "\n", "for i, val in enumerate(amplitudes):\n", " print('{0:16}: {1:1.3f}'.format(groupnames[i], val))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4. Performing XANES LCF\n", "\n", "We can perform LCF with the [lcf()](../../fit/fit_lcf.rst#araucaria.fit.lcfit.lcf) function. For clarity we use dictionaries to specify the normalization and lcf parameters for the fit. Note that we specify `fit_region='xanes'` and a `fit_range` with respect to a fixed absorption threshold in energy units.\n", "\n", "Once the LCF is finished we can print a summary with the [lcf_report()](../../fit/fit_lcf.rst#araucaria.fit.lcfit.lcf_report) function." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[Parameters]]\n", " fit_region = xanes\n", " fit_range = [7092, 7182]\n", " sum_one = False\n", "[[Groups]]\n", " scan = sample\n", " ref1 = Ferrihydrite_20K\n", " ref2 = Goethite_20K\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", " # function evals = 10\n", " # data points = 168\n", " # variables = 2\n", " chi-square = 1.5177e-07\n", " reduced chi-square = 9.1428e-10\n", " Akaike info crit = -3494.57580\n", " Bayesian info crit = -3488.32787\n", "[[Variables]]\n", " amp1: 0.40894498 +/- 9.9474e-05 (0.02%) (init = 0.5)\n", " amp2: 0.59225273 +/- 9.9337e-05 (0.02%) (init = 0.5)\n", "[[Correlations]] (unreported correlations are < 0.100)\n", " C(amp1, amp2) = -1.000\n" ] } ], "source": [ "from araucaria.fit import lcf, lcf_report\n", "\n", "# parameters for normalization and lcf\n", "k_edge = 7112\n", "pre_edge_kws = {'pre_range' : [-160, -40],\n", " 'post_range': [140, 950], \n", " 'nvict' : 0, \n", " 'e0' : k_edge}\n", "\n", "lcf_kws = {'fit_region': 'xanes',\n", " 'sum_one' : False,\n", " 'fit_range' : [k_edge-20,k_edge+70]}\n", "\n", "collection.apply(pre_edge, **pre_edge_kws)\n", "out = lcf(collection, **lcf_kws)\n", "print(lcf_report(out))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As seen in the report, our prediction for the amplitudes of the original signals is in agreement with the LCF results.\n", "\n", "We can visually compare the fitted curve with the original signal with the [fig_lcf()](../../plot_module.rst#araucaria.plot.fig_lcf.fig_lcf) function.\n", "The plot function accepts a dictionary to set the parameters for the XAFS figures (`fig_pars`), as well as a dictionary to set general figure parameters (`fig_kws`)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "from araucaria.plot import fig_lcf\n", "\n", "# figure parameters\n", "offset = 0.5\n", "fig_kws = {'figsize' : (8, 5)} # size figure\n", "fig_pars = {'e_range' : (k_edge-50, k_edge+90),\n", " 'e_ticks' : [k_edge-51, k_edge-3, k_edge+44, k_edge+91],\n", " 'prop_cycle': [{'color' : ['black', 'red', 'forestgreen'],\n", " 'linewidth' : [1.5 , 1.5 , 1.5 ],},\n", " {'color' : ['black', 'red', 'grey'],\n", " 'linewidth' : [1.5 , 1.5 , 1.5 ],}\n", " ]}\n", "\n", "fig, ax = fig_lcf(out, offset=offset, fig_pars=fig_pars, **fig_kws)\n", "fig.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5. Performing EXAFS LCF\n", "\n", "The [lcf()](../../fit/fit_lcf.rst#araucaria.fit.lcfit.lcf) function also allows LCF of the EXAFS spectrum by specifying the `fit_region='exafs'`. Note that here we additionally declare a dictionary for the background removal parameters, and modify `fit_range` to consider values in wavenumber space ($k$).\n", "\n", "Once the LCF is finished we can print a summary with the [lcf_report()](../../fit/fit_lcf.rst#araucaria.fit.lcfit.lcf_report) function, and plot the resulting fit with the [fig_lcf()](../../plot_module.rst#araucaria.plot.fig_lcf.fig_lcf) function." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[Parameters]]\n", " fit_region = exafs\n", " fit_range = [2, 12]\n", " sum_one = False\n", " kweight = 2\n", "[[Groups]]\n", " scan = sample\n", " ref1 = Ferrihydrite_20K\n", " ref2 = Goethite_20K\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", " # function evals = 10\n", " # data points = 201\n", " # variables = 2\n", " chi-square = 0.03903089\n", " reduced chi-square = 1.9614e-04\n", " Akaike info crit = -1713.88806\n", " Bayesian info crit = -1707.28145\n", "[[Variables]]\n", " amp1: 0.40386852 +/- 0.00361482 (0.90%) (init = 0.5)\n", " amp2: 0.59444931 +/- 0.00300275 (0.51%) (init = 0.5)\n", "[[Correlations]] (unreported correlations are < 0.100)\n", " C(amp1, amp2) = -0.923\n" ] } ], "source": [ "# parameters for normalization, background removal and lcf\n", "k_edge = 7112\n", "\n", "autobk_kws = {'k_range' : [0, 15],\n", " 'kweight' : 2,\n", " 'rbkg' : 1,\n", " 'win' : 'hanning',\n", " 'dk' : 0.1,\n", " 'clamp_hi' : 35}\n", "\n", "lcf_kws = {'fit_region': 'exafs',\n", " 'sum_one' : False,\n", " 'fit_range' : [2, 12]}\n", "\n", "collection.apply(autobk, **autobk_kws)\n", "out = lcf(collection, **lcf_kws)\n", "print(lcf_report(out))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# figure parameters\n", "offset = 2.0\n", "fig_kws = {'figsize' : (8, 5)} # size figure\n", "fig_pars = {'prop_cycle': [{'color' : ['black', 'red', 'forestgreen'],\n", " 'linewidth' : [1.5 , 1.5 , 1.5 ]},\n", " {'color' : ['black', 'red', 'grey'],\n", " 'linewidth' : [1.5 , 1.5 , 1.5 ]}\n", " ]}\n", "\n", "fig, ax = fig_lcf(out, offset=offset, fig_pars=fig_pars, **fig_kws)\n", "fig.tight_layout()\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.9.4" } }, "nbformat": 4, "nbformat_minor": 4 }