{ "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": null, "id": "cdd93a65-aaaf-4712-8ce0-0f171461d3ac", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "installing pyTEMlib\n", "^C\n", "done\n", "Collecting pyTEMlib\n", " Using cached pytemlib-0.2025.9.1-py3-none-any.whl.metadata (3.6 kB)\n", "Requirement already satisfied: scipy in 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----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", 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" ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 [dask]\n", " ---- ----------------------------------- 1/10 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------------------------------- 2/10 [gdown]\n", " -------- ------------------------------- 2/10 [gdown]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ------------ --------------------------- 3/10 [distributed]\n", " ---------------- ----------------------- 4/10 [ipympl]\n", " -------------------- ------------------- 5/10 [sidpy]\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "ERROR: Could not install packages due to an OSError: [WinError 32] The process cannot access the file because it is being used by another process: 'C:\\\\Users\\\\gduscher\\\\AppData\\\\Local\\\\anaconda3\\\\envs\\\\pytem\\\\Lib\\\\site-packages\\\\sidpy\\\\base\\\\num_utils.py'\n", "Consider using the `--user` option or check the permissions.\n", "\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') < '0.2025.11.0':\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": [ "You don't have igor2 installed. If you wish to open igor files, you will need to install it (pip install igor2) before attempting.\n", "pyTEM version: 0.2025.12.0\n" ] } ], "source": [ "%matplotlib widget\n", "import sys\n", "import numpy as np\n", "import matplotlib.pylab as plt\n", "\n", "# using pyTEMlib.eds_tools, pyTEMlib.file_tools and pyTEMlib.eels_tools (for line definitions)\n", "sys.path.insert(0, '..//..//')\n", "sys.path.insert(0, '..//..//..//SciFiReaders//')\n", "\n", "\n", "%load_ext autoreload\n", "%autoreload 2\n", "import SciFiReaders\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__ = '2025_10_27'" ] }, { "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": "e018cdbfb6fa441bbd340d98a930627e", "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": "8cbdb566de084eaebba05b4b937e54fd", "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": 3, "id": "ca3ac166-4d89-439e-86bf-a910df2202ac", "metadata": { "scrolled": true }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "beb3156580964118acef1268985237ca", "version_major": 2, "version_minor": 0 }, "image/png": 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", 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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', 'O', 'Ti']" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6877c665b1cd4a40b6f3b2001df02d4a", "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": 31, "id": "a99944c9-ca3a-4a19-bae3-5839329ccb51", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "15e447f7c74d4e0cab1525a71a254a7a", "version_major": 2, "version_minor": 0 }, "image/png": 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", "text/html": [ "\n", "
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\n", " Figure\n", "
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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": 32, "id": "dae802b1-1fa4-4c1d-9d7c-eaaaf2e26790", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "using cross sections for quantification\n", "Sr: 23.42 at% 47.63 wt%\n", "Ti: 32.34 at% 35.95 wt%\n", "O : 44.24 at% 16.43 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": 33, "id": "89413148", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "using k-factors for quantification\n", "Sr: 25.22 at% 52.20 wt%\n", "O : 48.84 at% 18.46 wt%\n", "Ti: 25.94 at% 29.34 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 }