{ "cells": [ { "cell_type": "markdown", "id": "90e0cd4a-149a-43e5-808f-b32b27d7ca5a", "metadata": {}, "source": [ " **EDS_Tools: [Spectroscopy](../4_EELS_Tools.ipynb)** \n", "\n", "
\n", "\n", "# Analysis of EDS Spectra\n", "
\n", "\n", "[](https://raw.githubusercontent.com/pycroscopy/pyTEMlib/main/notebooks/Spectroscopy/EDS.ipynb) \n", "\n", "[![OpenInColab](https://colab.research.google.com/assets/colab-badge.svg)](\n", " https://colab.research.google.com/github/pycroscopy/pyTEMlib/blob/main/notebooks/Spectroscopy/EDS.ipynb)\n", " \n", "part of \n", "\n", " **[pyTEMlib](https://pycroscopy.github.io/pyTEMlib/about.html)**\n", "\n", "a [pycroscopy](https://pycroscopy.github.io/pycroscopy/about.html) ecosystem package\n", "\n", "\n", "\n", "Notebook by Gerd Duscher, 2025\n", "\n", "Microscopy Facilities
\n", "Institute of Advanced Materials & Manufacturing
\n", "The University of Tennessee, Knoxville\n", "\n", "Model based analysis and quantification of data acquired with transmission electron microscopes\n", "\n", "## Content\n", "An Introduction into displaying and analyzing EDS spectrum images and spectra\n", "This works also on Google Colab.\n", "\n", "\n", "## Prerequesites\n", "\n", "### Install pyTEMlib\n", "\n", "If you have not done so in the [Introduction Notebook](_.ipynb), please test and install [pyTEMlib](https://github.com/gduscher/pyTEMlib) and other important packages with the code cell below.\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "cdd93a65-aaaf-4712-8ce0-0f171461d3ac", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "done\n" ] } ], "source": [ "import sys\n", "import importlib.metadata\n", "\n", "def test_package(package_name):\n", " \"\"\"Test if package exists and returns version or -1\"\"\"\n", " try:\n", " version = importlib.metadata.version(package_name)\n", " except importlib.metadata.PackageNotFoundError:\n", " version = '-1'\n", " return version\n", "\n", "\n", "# pyTEMlib setup ------------------\n", "if test_package('pyTEMlib') < '0.2026.1.1':\n", " print('installing pyTEMlib')\n", " \n", " !{sys.executable} -m pip install pyTEMlib --upgrade\n", "# ------------------------------\n", "print('done')" ] }, { "cell_type": "markdown", "id": "c65aa1df-a08e-418e-a3c9-2486ac4cd262", "metadata": {}, "source": [ "### Loading of necessary libraries\n", "\n", "Please note, that we only need to load the pyTEMlib library, which is based on sidpy Datsets.\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "a05abc73-c41d-41ff-80fa-f1e9c5a300c2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pyTEM version: 0.2026.1.1\n" ] } ], "source": [ "%matplotlib widget\n", "import sys\n", "import numpy as np\n", "import matplotlib.pylab as plt\n", "\n", "import pyTEMlib\n", "\n", "if 'google.colab' in sys.modules:\n", " from google.colab import output\n", " output.enable_custom_widget_manager()\n", " from google.colab import drive\n", "\n", "if 'google.colab' in sys.modules:\n", " drive.mount(\"/content/drive\")\n", "\n", "# For archiving reasons it is a good idea to print the version numbers out at this point\n", "print('pyTEM version: ',pyTEMlib.__version__)\n", "__notebook__ = 'EDS_Spectrum_Analysis'\n", "__notebook_version__ = '2026_1_19'" ] }, { "cell_type": "markdown", "id": "f28810e4-818e-4f78-affc-d543d9a79735", "metadata": {}, "source": [ "## Open File\n", "\n", "### Load File\n", "\n", "Select a main dataset and any additional data like reference data and such." ] }, { "cell_type": "code", "execution_count": 2, "id": "1a13daae-0f41-4be4-8b94-ecca0476055b", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1c231b3c433e4833a5c881cd484a0c3f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(Dropdown(description='directory:', layout=Layout(width='90%'), options=('C:\\\\Users\\\\gduscher\\\\O…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "fb04e3d9b05d45e08169a2b02a349911", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Select Main', layout=Layout(grid_area='header', width='auto'), style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# C:\\Users\\gduscher\\OneDrive - University of Tennessee\\google_drive\\2022 Experiments\\Spectra\\20221214\\AlCe-200kV\n", "fileWidget = pyTEMlib.file_tools.FileWidget()" ] }, { "cell_type": "markdown", "id": "0e219615-5ff8-4064-a99b-6dffd5240fac", "metadata": {}, "source": [ "### Select and Plot Dataset\n", "\n", "Select a dataset from the drop down value and display it with the code cell below.\n", "\n", "Here we sum the spectra of the 4 quadrants and define the detector parameter." ] }, { "cell_type": "code", "execution_count": 5, "id": "ca3ac166-4d89-439e-86bf-a910df2202ac", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ce8007e6f0754ae1acf6a777213efb8c", "version_major": 2, "version_minor": 0 }, "image/png": 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", "text/html": [ "\n", "
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You may want to use the da.map_blocks function or something similar to silence this warning. Your code may stop working in a future release.\n", " warnings.warn(\n" ] }, { "data": { "text/plain": [ "['Sr', 'Cu', 'Ti', 'O']" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1e601f6a6d85488ab5a7c932dce6428c", "version_major": 2, "version_minor": 0 }, "image/png": 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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": [ "# --------Input -----------\n", "minimum_number_of_peaks = 10\n", "# --------------------------\n", "\n", "elements = pyTEMlib.eds_tools.get_elements(spectrum, minimum_number_of_peaks, verbose=False)\n", "\n", "plt.figure()\n", "plt.plot(spectrum.energy_scale,spectrum, label = 'spectrum')\n", "pyTEMlib.eds_tools.plot_lines(spectrum.metadata['EDS'], plt.gca())\n", "plt.legend();\n", "elements" ] }, { "cell_type": "markdown", "id": "5711fa8c-6d5b-4c8b-8bb9-dc4be17dcc4a", "metadata": {}, "source": [ "## Quantify\n", "\n", "### Fit spectrum" ] }, { "cell_type": "code", "execution_count": 47, "id": "a99944c9-ca3a-4a19-bae3-5839329ccb51", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "no intensity Nb M-family\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e88bd291a2284215af36f260df7e618e", "version_major": 2, "version_minor": 0 }, "image/png": 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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": [ "peaks, pp = pyTEMlib.eds_tools.fit_model(spectrum, use_detector_efficiency=True)\n", "model = pyTEMlib.eds_tools.get_model(spectrum)\n", "\n", "plt.figure()\n", "plt.plot(spectrum.energy_scale.values, spectrum, label='spectrum')\n", "plt.plot(spectrum.energy_scale.values, model, label='model')\n", "plt.plot(spectrum.energy_scale.values, np.array(spectrum)-np.array(model), label='difference')\n", "plt.xlabel('energy (eV)')\n", "pyTEMlib.eds_tools.plot_lines(spectrum.metadata['EDS'], plt.gca())\n", "plt.axhline(y=0, xmin=0, xmax=1, color='gray')\n", "plt.legend();" ] }, { "cell_type": "markdown", "id": "b64773b9-eb7b-47e0-b5ac-961e608f7ceb", "metadata": {}, "source": [ "### Quantify Spectrum\n", "first with Bote-Salvat cross section\n", "using dictionaries calculated with [emtables package](https://github.com/adriente/emtables/blob/main/)." ] }, { "cell_type": "code", "execution_count": 48, "id": "dae802b1-1fa4-4c1d-9d7c-eaaaf2e26790", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "using cross sections for quantification\n", "Sr: 20.51 at% 45.05 wt%\n", "Ti: 28.35 at% 34.02 wt%\n", "O : 50.91 at% 20.42 wt%\n", "Nb: 0.22 at% 0.52 wt%\n" ] } ], "source": [ "pyTEMlib.eds_tools.quantify_eds(spectrum, mask =['Cu'])" ] }, { "cell_type": "markdown", "id": "21774512", "metadata": {}, "source": [ "then with k-factor dictionary" ] }, { "cell_type": "code", "execution_count": 49, "id": "89413148", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "using k-factors for quantification\n", "Sr: 21.80 at% 48.98 wt%\n", "Ti: 22.43 at% 27.54 wt%\n", "O : 55.46 at% 22.75 wt%\n", "Nb: 0.30 at% 0.73 wt%\n", "excluded from quantification ['Cu']\n" ] } ], "source": [ "q_dict = pyTEMlib.eds_tools.load_k_factors()\n", "tags = pyTEMlib.eds_tools.quantify_eds(spectrum, q_dict, mask = ['Cu'])" ] }, { "cell_type": "markdown", "id": "59987821", "metadata": {}, "source": [ "### Absorption Correction\n", "Lower energy lines will be more affected than higher x-ray lines.\n", "\n", "At thin sample location (<50nm) absorption is not significant." ] }, { "cell_type": "code", "execution_count": 34, "id": "7f9e23cc-bbb2-47d1-ae3b-7ec78532aeec", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Element: Sr, Corrected Atom%: 19.59, Corrected Weight%: 47.03\n", "Element: Cu, Corrected Atom%: 0.00, Corrected Weight%: 0.00\n", "Element: Ti, Corrected Atom%: 20.29, Corrected Weight%: 26.61\n", "Element: O, Corrected Atom%: 60.12, Corrected Weight%: 26.35\n" ] } ], "source": [ "# ------ Input ----------\n", "thickness_in_nm = 250\n", "# -----------------------\n", "pyTEMlib.eds_tools.apply_absorption_correction(spectrum, thickness_in_nm)\n", "for key, value in spectrum.metadata['EDS']['GUI'].items():\n", " if 'corrected-atom%' in value:\n", " print(f\"Element: {key}, Corrected Atom%: {value['corrected-atom%']:.2f}, Corrected Weight%: {value['corrected-weight%']:.2f}\")" ] }, { "cell_type": "markdown", "id": "b67c5bd2-2950-44fc-a2d1-10789eb71920", "metadata": {}, "source": [ "## Summary\n", "The spectrum is modeled completely with background and characteristic peak-families.\n", "\n", "Either \n", "- k-factors in a file (here from Spectra300) or\n", "- Bothe-Salvat cross-sections\n", " \n", "are used for quantification.\n", "\n", "## Appendix\n", "### Background\n", "The determined background used for the model-based quantification is based on the detector effciency.\n", "\n", "Note:\n", "\n", "The detector efficiency is also used for the quantification model.\n" ] } ], "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": "3.13.5" } }, "nbformat": 4, "nbformat_minor": 5 }