espm: The Electron Spectro-Microscopy Python Library
The espm package is designed for simulation and physics-guided NMF decomposition of hyperspectral data. Even though the package is mainly centered around electron spectro-microscopy applications, custom models can be implemented for other type of data. Currently espm supports the simulation and analysis of simultaneous scanning transmission electron microscopy and energy dispersive X-ray spectroscopy (STEM / EDXS). In future implementation, we will try to extend the package to support electron energy loss spectroscopy (EELS).
The main components of the package are:
- The simulation of STEM-EDXS datasets using espm.datasets which combines espm.weights for the simulation of spatial distributions and èspm.models for the simulation of spectra.
- The hyperspectral unmixing of STEM-EDXS spectrum images using espm.estimators. This module contains algorithms to perform non-negative matrix factorization with diverse regularisation (e.g. Laplacian or L1) and contraints (e.g. simplex).
- The espm.models module can also be used to perform a physics-guided decomposition of STEM-EDXS datasets.
Installation
You can install this package from PyPi using:
$ pip install espm
If you want to develop, please use the option:
$ git clone https://github.com/adriente/espm.git
$ cd espm
$ pip install cython
$ pip install -e ."[dev]"
Getting started
Try the api.ipynb notebook in the notebooks folder.
Documentation
The documentation is available at https://espm.readthedocs.io/en/latest/
You can get started with the following notebooks:
Simulate STEM-EDXS data : https://espm.readthedocs.io/en/latest/introduction/notebooks/generate_data.ipynb
Physics-guided decomposition (ESpM-NMF) STEM-EDXS data : https://espm.readthedocs.io/en/latest/introduction/notebooks/api.html
Tests of the ESpM-NMF with a toy dataset : https://espm.readthedocs.io/en/latest/introduction/notebooks/toy-problem.html
CITING
If you use this library, please cite the following paper:
@article{teurtrie2023espm,
title={espm: A Python library for the simulation of STEM-EDXS datasets},
author={Teurtrie, Adrien and Perraudin, Nathana{\"e}l and Holvoet, Thomas and Chen, Hui and Alexander, Duncan TL and Obozinski, Guillaume and H{\'e}bert, C{\'e}cile},
journal={Ultramicroscopy},
pages={113719},
year={2023},
publisher={Elsevier}
}