{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Reading IXAS spectra\n", "\n", "*Last update: June 2021*\n", "\n", "The International X-ray Absorption Society ([IXAS](https://xrayabsorption.org)) maintains a [data library of XAFS spectra](https://xaslib.xrayabsorption.org/elem/) for several compounds and absorption edges. In this tutorial we will be accesing the library to retrieve, process and visualize spectra in `araucaria`.\n", "\n", "This notebook explains the the following steps:\n", "\n", "1. Read spectra from a uniform resource locator (URL).\n", "2. Create a collection with the spectra.\n", "3. Plot processed spectra." ] }, { "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": [ "from araucaria.utils import get_version\n", "print(get_version(dependencies=True))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reading spectra directly from a URL\n", "\n", "The IXAS data library offers a [website frontend](https://xaslib.xrayabsorption.org/elem/) to download data. However, we will be reading it directly with assistance of the [urllib](https://docs.python.org/3/library/urllib.html) module of Python.\n", "\n", "We will be retrieving the following spectra acquired at the Se K-edge in the Advanced Photon Source synchrotron:\n", "\n", "| Name | ID |\n", "| :- | --- |\n", "| Se_CoSe_rt_01 | 185 |\n", "| Se_Cu2Se_rt_01 | 187 |\n", "| Se_CuSe_rt_01 | 189 |\n", "\n", "\n", "For convenience we will first create a dictionary with the names and ID of spectra. This dictionary will then be used to construct a list with the URLs:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "https://xaslib.xrayabsorption.org/rawfile/185/Se_CoSe_rt_01.xdi\n", "https://xaslib.xrayabsorption.org/rawfile/187/Se_Cu2Se_rt_01.xdi\n", "https://xaslib.xrayabsorption.org/rawfile/189/Se_CuSe_rt_01.xdi\n" ] } ], "source": [ "from os.path import join\n", "\n", "# dictionary with names and IDs\n", "dvals = {'names': ['Se_'+name+'_rt_01.xdi' for name in ['CoSe', 'Cu2Se', 'CuSe'] ],\n", " 'id' : ['185' , '187', '189']}\n", "\n", "# constructing urls\n", "urls = []\n", "static = 'https://xaslib.xrayabsorption.org/rawfile/'\n", "for i, name in enumerate(dvals['names']):\n", " urls.append(static + dvals['id'][i] + '/' + name)\n", "\n", "# printing urls\n", "for val in urls:\n", " print(val)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", " \n", " **Note**\n", " \n", " If you want to access diferent spectra, just modify the dictionary with the names and ID of the desired spectra. Such values are available at the IXAS data library website.\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before reading the data we need to assess the file format: For this we will print the header of the last file with the [urlopen()](https://docs.python.org/3/library/urllib.request.html#urllib.request.urlopen) function of urllib: " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 b'#XDI/1.1 GSE/1.0'\n", "1 b'# Column.1: energy eV'\n", "2 b'# Column.2: itrans'\n", "3 b'# Column.3: i0'\n", "4 b'# Scan.end_time: 2008-04-10 19:35:01'\n", "5 b'# Scan.start_time: 2008-04-10 19:17:19'\n", "6 b'# Element.symbol: Se'\n", "7 b'# Element.edge: K'\n", "8 b'# Mono.d_spacing: 3.13555'\n", "9 b'# Mono.name: Si 111'\n", "10 b'# Sample.temperature: room temperature'\n", "11 b'# Sample.formula: CuSe'\n", "12 b'# Sample.name: copper selenide, klockmannite'\n", "13 b'# Sample.prep: powder on tape, many layers'\n", "14 b'# Facility.Name: APS'\n", "15 b'# Beamline.Name: 13-BM-D'\n", "16 b'# Beamline.xray_source: bending magnet'\n", "17 b'# Beamline.Storage_Ring_Current: 101.873'\n", "18 b'# Beamline.I0: N2, 5 nA/V'\n", "19 b'# Beamline.I1: N2, 20 nA/V'\n", "20 b'# ScanParameters.E0: 12658.0'\n", "21 b'# ScanParameters.Legend: Start Stop Step Npts Time Kspace?'\n", "22 b'# ScanParameters.Region1: -150.00 -10.000 5.0000 29.000 2.0000 0'\n", "23 b'# ScanParameters.Region2: -10.000 30.000 0.25000 161.00 2.0000 0'\n", "24 b'# ScanParameters.Region3: 2.8061 14.000 0.039978 281.00 2.0000 1'\n", "25 b'#-------------------------'\n", "26 b'# energy itrans i0'\n", "27 b'12508.000 493738.40 120177.40'\n", "28 b'12513.000 490898.40 119360.40'\n", "29 b'12518.000 489640.40 118973.40'\n" ] } ], "source": [ "from urllib.request import urlopen\n", "\n", "response = urlopen(val)\n", "for i in range(30):\n", " print(i, response.readline().strip())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As seen from the output (line 26), the file contains columns for the energy, transmitted intensity (itrans), and reference itensity (i0), in that order." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create a collection with the spectra\n", "\n", "We will use the [read_rawfile()](../../io/io_read.rst#araucaria.io.io_read.read_rawfile) function to read the files and include them in a [Collection](../../main/main_collection.rst#araucaria.main.collection.Collection):\n", "\n", "- We first create an empty [Collection](../../main/main_collection.rst#araucaria.main.collection.Collection) to contain our group datasets.\n", "- Contents of the remote file are accessed with the [urlopen()](https://docs.python.org/3/library/urllib.request.html#urllib.request.urlopen) funcion.\n", "- Contents are directly passed to the [read_rawfile()](../../io/io_read.rst#araucaria.io.io_read.read_rawfile) function, and a [Group](../../main/main_group.rst#araucaria.main.group.Group) dataset is returned.\n", "- The group is then added to the collection with the [add_group()](../../main/main_collection.rst#araucaria.main.collection.Collection.add_group) method.\n", "- Once the collection has been populated, a summary report is requested with the [sumary()](../../main/main_collection.rst#araucaria.main.collection.Collection.summary) method." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=======================================\n", "id dataset tag mode n \n", "=======================================\n", "1 Se_CoSe_rt_01.xdi scan mu 1 \n", "2 Se_Cu2Se_rt_01.xdi scan mu 1 \n", "3 Se_CuSe_rt_01.xdi scan mu 1 \n", "=======================================\n" ] } ], "source": [ "from araucaria import Collection\n", "from araucaria.io import read_rawfile\n", "\n", "collection = Collection()\n", "for i, url in enumerate(urls):\n", " content = urlopen(url)\n", " group = read_rawfile(content, usecols=(0,2,1), scan='mu', ref=False, tol=1e-4)\n", " collection.add_group(group)\n", "\n", "report = collection.summary()\n", "report.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", " \n", " **Warning**\n", " \n", " URLs or file formats in databases may change over time or between files. It is your responsibility to check the validty of the URL or the file format before reading and processing spectra.\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot processed spectra\n", "\n", "Prior to plotting we need to compute the normalized spectra and the extended x-ray fine strucure (EXAFS) spetra. For this we use the [apply()](../../main/main_collection.rst#araucaria.main.collection.Collection.apply) method to process spectra in the collection.\n", "\n", "We then use the [fig_xas_template()](../../plot_module.rst#araucaria.plot.template.fig_xas_template) function to create a `Figure` and `axes` objects with the following attributes:\n", "\n", "- 2 pre-defined panels for XANES and EXAFS spectra.\n", "- A dictionary to specify figure decorators (figpars).\n", "- A dictionary to specify additional parameters of the `matplotlib` figure (fig_kws). \n", "\n", "Once the figure template has been created, we use a loop to populate the axes with the respective plot artists." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from araucaria.xas import pre_edge, autobk\n", "from araucaria.plot import fig_xas_template\n", "import matplotlib.pyplot as plt\n", "\n", "# normalizing spectra and extracting exafs\n", "collection.apply(pre_edge)\n", "collection.apply(autobk)\n", "\n", "# figure decorators\n", "kweight = 2\n", "offset = [1.1, 1.8]\n", "figpars = {'e_range' : (12600, 12750),\n", " 'k_range' : (0, 15),\n", " 'kweight' : kweight}\n", "fig_kws = {'figsize' : (7.5,5)}\n", "\n", "# declaring figure and populating axes\n", "fig, ax = fig_xas_template(panels='xe', fig_pars=figpars, **fig_kws)\n", "names = collection.get_names()\n", "for i, name in enumerate(names):\n", " group = collection.get_group(name)\n", " ax[0].plot(group.energy, group.norm + i*offset[0], label=name.split('_')[1])\n", " ax[1].plot(group.k, group.k**kweight*group.chi + i*offset[1], label=name.split('_')[1])\n", "\n", "ax[0].legend(loc='lower right', edgecolor='k', fontsize=8)\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 }