EELS_Tools: Spectroscopy
Analysis of EDS Spectra#
part of
a pycroscopy ecosystem package
Notebook by Gerd Duscher, 2025
Microscopy Facilities
Institute of Advanced Materials & Manufacturing
The University of Tennessee, Knoxville
Model based analysis and quantification of data acquired with transmission electron microscopes
Content#
An Introduction into displaying and analyzing EDS spectrum images and spectra This works also on Google Colab.
Prerequesites#
Install pyTEMlib#
If you have not done so in the Introduction Notebook, please test and install pyTEMlib and other important packages with the code cell below.
import sys
import importlib.metadata
def test_package(package_name):
"""Test if package exists and returns version or -1"""
try:
version = importlib.metadata.version(package_name)
except importlib.metadata.PackageNotFoundError:
version = '-1'
return version
# pyTEMlib setup ------------------
if test_package('pyTEMlib') < '0.2025.10.0':
print('installing pyTEMlib')
!{sys.executable} -m pip install pyTEMlib -q --upgrade
# ------------------------------
print('done')
done
!{sys.executable} -m pip install git+https://github.com/pycroscopy/SciFiReaders/ --no-deps
Collecting git+https://github.com/pycroscopy/SciFiReaders/
Cloning https://github.com/pycroscopy/SciFiReaders/ to /tmp/pip-req-build-maj59wix
Running command git clone --filter=blob:none --quiet https://github.com/pycroscopy/SciFiReaders/ /tmp/pip-req-build-maj59wix
Resolved https://github.com/pycroscopy/SciFiReaders/ to commit 4fc1a99f15114d0dbad99884247b01197899d37d
Installing build dependencies ... ?25l-
\
done
?25h Getting requirements to build wheel ... ?25l-
done
?25h Preparing metadata (pyproject.toml) ... ?25l-
done
?25hBuilding wheels for collected packages: SciFiReaders
Building wheel for SciFiReaders (pyproject.toml) ... ?25l-
done
?25h Created wheel for SciFiReaders: filename=scifireaders-0.12.2-py3-none-any.whl size=127115 sha256=4bdd96335989e5daefb1df7b8859e67a98074801bafa94f0ecd35c6b8e55dacd
Stored in directory: /tmp/pip-ephem-wheel-cache-kb6pneqq/wheels/b4/84/c4/7386938655486403e7ad8b9c649ec41c0437a6338e85b82e64
Successfully built SciFiReaders
Installing collected packages: SciFiReaders
Attempting uninstall: SciFiReaders
Found existing installation: SciFiReaders 0.12.0
Uninstalling SciFiReaders-0.12.0:
Successfully uninstalled SciFiReaders-0.12.0
Successfully installed SciFiReaders-0.12.2
[notice] A new release of pip is available: 25.2 -> 25.3
[notice] To update, run: pip install --upgrade pip
Loading of necessary libraries#
Please note, that we only need to load the pyTEMlib library, which is based on sidpy Datsets.
%matplotlib widget
import sys
import numpy as np
import matplotlib.pylab as plt
# using pyTEMlib.eds_tools, pyTEMlib.file_tools and pyTEMlib.eels_tools (for line definitions)
sys.path.insert(0, '..//..//')
sys.path.insert(0, '..//..//..//SciFiReaders//')
%load_ext autoreload
%autoreload 2
import SciFiReaders
import pyTEMlib
if 'google.colab' in sys.modules:
from google.colab import output
output.enable_custom_widget_manager()
from google.colab import drive
if 'google.colab' in sys.modules:
drive.mount("/content/drive")
# For archiving reasons it is a good idea to print the version numbers out at this point
print('pyTEM version: ',pyTEMlib.__version__)
__notebook__ = 'EDS_Spectrum_Analysis'
__notebook_version__ = '2025_10_27'
You don't have igor2 installed. If you wish to open igor files, you will need to install it (pip install igor2) before attempting.
You don't have gwyfile installed. If you wish to open .gwy files, you will need to install it (pip install gwyfile) before attempting.
pyTEM version: 0.2025.10.1
Open File#
Load File#
Select a main dataset and any additional data like reference data and such.
print('pyTEM version: ',pyTEMlib.__version__)
pyTEM version: 0.2025.10.1
fileWidget = pyTEMlib.file_tools.FileWidget()
Select and Plot Dataset#
Select a dataset from the drop down value and display it with the code cell below.
Here we sum the spectra of the 4 quadrants and define the detector parameter.
help(fileWidget)
Help on FileWidget in module pyTEMlib.file_tools object:
class FileWidget(sidpy.io.interface_utils.FileWidget)
| FileWidget(dir_name=None, extension=['*'], sum_frames=False)
|
| Widget to select directories or widgets from a list
|
| Works in google colab.
| The widget converts the name of the nion file to the one in Nion's swift software,
| because it is otherwise incomprehensible
|
| Attributes
| ----------
| dir_name: str
| name of starting directory
| extension: list of str
| extensions of files to be listed in widget
|
| Methods
| -------
| get_directory
| set_options
| get_file_name
|
| Example
| -------
| >>from google.colab import drive
| >>drive.mount("/content/drive")
| >>file_list = pyTEMlib.file_tools.FileWidget()
| next code cell:
| >>datasets = file_list.datasets
| >>dataset = file_list.selected_dataset
|
| Method resolution order:
| FileWidget
| sidpy.io.interface_utils.FileWidget
| builtins.object
|
| Methods defined here:
|
| __init__(self, dir_name=None, extension=['*'], sum_frames=False)
| Initialize self. See help(type(self)) for accurate signature.
|
| add_dataset(self, value: int = 0)
| Add another dataset to the list of loaded datasets.
|
| select_dataset(self, value: int = 0)
| Select a dataset from the dropdown.
|
| select_main(self, value: int = 0)
| Select the main dataset.
|
| ----------------------------------------------------------------------
| Methods inherited from sidpy.io.interface_utils.FileWidget:
|
| get_directory(self, directory=None)
|
| get_file_name(self, b)
|
| set_dir(self, value=0)
|
| set_options(self)
|
| update_directory_list(self, directory_name)
| Update the list of files in the current directory
| Cam be overwritten in child class
|
| ----------------------------------------------------------------------
| Data descriptors inherited from sidpy.io.interface_utils.FileWidget:
|
| __dict__
| dictionary for instance variables
|
| __weakref__
| list of weak references to the object
spectrum = fileWidget.selected_dataset
start = np.searchsorted(spectrum.energy_scale.values, 100)
energy_scale = spectrum.energy_scale.values[start:]
detector_Efficiency= pyTEMlib.eds_tools.detector_response(spectrum) # tags, spectrum.energy_scale.values[start:])
if 'start_energy' not in spectrum.metadata['EDS']['detector']:
spectrum.metadata['EDS']['detector']['start_energy'] = 120
spectrum[:np.searchsorted(spectrum.energy_scale.values,spectrum.metadata['EDS']['detector']['start_energy'])] = 0.
spectrum.metadata['EDS']['detector']['detector_efficiency'] = detector_Efficiency
view = spectrum.plot()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[7], line 2
1 spectrum = fileWidget.selected_dataset
----> 2 start = np.searchsorted(spectrum.energy_scale.values, 100)
3 energy_scale = spectrum.energy_scale.values[start:]
4 detector_Efficiency= pyTEMlib.eds_tools.detector_response(spectrum) # tags, spectrum.energy_scale.values[start:])
AttributeError: 'NoneType' object has no attribute 'energy_scale'
Find Elements#
# --------Input -----------
minimum_number_of_peaks = 10
# --------------------------
minor_peaks = pyTEMlib.eds_tools.detect_peaks(spectrum, minimum_number_of_peaks=minimum_number_of_peaks)
keys = list(spectrum.metadata['EDS'].keys())
for key in keys:
if len(key) < 3:
del spectrum.metadata['EDS'][key]
elements = pyTEMlib.eds_tools.find_elements(spectrum, minor_peaks)
print(elements)
spectrum.metadata['EDS'].update(pyTEMlib.eds_tools.get_x_ray_lines(spectrum, elements))
plt.figure()
plt.plot(spectrum.energy_scale,spectrum, label = 'spectrum')
pyTEMlib.eds_tools.plot_lines(spectrum.metadata['EDS'], plt.gca())
c:\Users\gduscher\AppData\Local\anaconda3\Lib\site-packages\dask\array\core.py:1744: FutureWarning: The `numpy.argsort` function is not implemented by Dask array. 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.
warnings.warn(
['Cu', 'O', 'Ti', 'Sr']
Quantify#
Fit spectrum#
peaks, pp = pyTEMlib.eds_tools.fit_model(spectrum, use_detector_efficiency=True)
model = pyTEMlib.eds_tools.get_model(spectrum)
plt.figure()
plt.plot(spectrum.energy_scale, spectrum, label='spectrum')
plt.plot(spectrum.energy_scale, model, label='model')
plt.plot(spectrum.energy_scale, spectrum-model, label='difference')
plt.xlabel('energy (eV)')
pyTEMlib.eds_tools.plot_lines(spectrum.metadata['EDS'], plt.gca())
plt.axhline(y=0, xmin=0, xmax=1, color='gray')
plt.legend()
<matplotlib.legend.Legend at 0x151bf38e350>
Quantify Spectrum#
first with Bote-Salvat cross section using dictionaries calculated with emtables package.
pyTEMlib.eds_tools.quantify_EDS(spectrum, mask =['Cu'])
using cross sections for quantification
O : 51.02 at% 20.51 wt%
Ti: 28.42 at% 34.20 wt%
Sr: 20.57 at% 45.29 wt%
then with k-factor dictionary
q_dict = pyTEMlib.eds_tools.load_k_factors()
tags = pyTEMlib.eds_tools.quantify_EDS(spectrum, q_dict, mask = ['Cu'])
using k-factors for quantification
O : 55.62 at% 22.91 wt%
Ti: 22.51 at% 27.74 wt%
Sr: 21.87 at% 49.34 wt%
excluded from quantification ['Cu']
Absorption Correction#
Lower energy lines will be more affected than higher x-ray lines.
At thin sample location (<50nm) absorption is not significant.
# ------ Input ----------
thickness_in_nm = 30
# -----------------------
pyTEMlib.eds_tools.apply_absorption_correction(spectrum, thickness_in_nm)
for key, value in spectrum.metadata['EDS']['GUI'].items():
if 'corrected-atom%' in value:
print(f"Element: {key}, Corrected Atom%: {value['corrected-atom%']:.2f}, Corrected Weight%: {value['corrected-weight%']:.2f}")
Element: Cu, Corrected Atom%: 0.00, Corrected Weight%: 0.00
Element: O, Corrected Atom%: 54.85, Corrected Weight%: 22.36
Element: Ti, Corrected Atom%: 22.90, Corrected Weight%: 27.94
Element: Sr, Corrected Atom%: 22.26, Corrected Weight%: 49.70
Summary#
The spectrum is modeled completely with background and characteristic peak-families.
Either
k-factors in a file (here from Spectra300) or
Bothe-Salvat cross-sections
are used for quantification.
Appendix#
Background#
The determined background used for the model-based quantification is based on the detector effciency.
Note:
The detector efficiency is also used for the quantification model.