exafs-neo 2.0.8

exafs_neo AI analysis using GA

EXAFS Neo

Versions: 2.0.8

Last update: May 1, 2024

EXAFS Neo utilize Genetic algorithm in fitting Extended X-ray absorption fine structure(EXAFS).

Prerequisites

It is highly recommend to utilize anaconda or pip package managers to prevent unforeseen dependency conflicts. EXAFS Neo uses larch to process the x-ray spectrum.

• Python: => 3.9
• Numpy: => 1.20
• Scipy: => 1.6
• Larch: > 0.9.47
• Matplotlib: > 3.0

It is highly recommend to create a new environment in anaconda to run EXAFS Neo to prevent packages conflicts. For Windows operating system, if you encounter an issue requiring "Microsoft C++ 14.0 or greater is needed", please download the tools at the following location C++ Tools and make sure to select C++ build tools during installation process.

if you are on a Mac (either Intel or M1), you need to make sure that xcode command line tools is install, if not input this command into terminal:

    xcode-select --install

Dependencies

EXAFS Neo requires the following dependencies to run:

Notes: this step below is only needed if you are installing using source code

    # Create new anaconda environment
    conda create -y --name exafs python=>3.9.10
    conda activate exafs
    conda install -y -c conda-forge "numpy>=1.23" "scipy>=1.8" "matplotlib>=3.6" "h5py>=3.5" "wxpython>=4.1" scikit-image scikit-learn pycifrw pandas jupyter plotly pyparsing pytest pytest-cov coverage
    pip install lmfit peakutils pyepics pyshortcuts termcolor sphinx dill pycifrw xraydb wxmplot wxutils fabio silx imageio charset-normalizer
    pip install xraylarch

Installations

To install via PIP:

    conda create --name "exafs_neo" "python=3.12"
    conda activate exafs_neo
    pip install exafs_neo

To install EXAFS Neo, simply clone the repo:

    git clone https://github.com/laumiulun/EXAFS_Neo.git
    cd EXAFS_Neo/
    pip install .

Usage

To run the included test suite, use the following command:

    ./run_tests

To run a sample test, make sure the environment is set correctly, and select an input file:

     exafs_neo -i test_inputs/test_temp.ini

Alternatively, you can also run EXAFS Neo in a jupyter notebook, please follow the example in the example/jupyter folder

GUI

We also have provided a GUI for use in additions to our program, with additional helper script to facilitate post-analysis. To use the GUI:

    exafs_neo_gui

The datafile requires header contain at least either a combination of (k, chi) or (energy, mu). It also requires a minimum of one newline for it to work correctly. An example of the correct header is as follows:

    #---------------------------------------------------------------------
    #  k chi chik chik2 chik3 win energy

Self adsorption correction

EXAFS also provides an internal option to perform self-adsorption on the sample file using Booth et al. correction. This is performed using git submodules:

    git submodule update --init --recursive
    cd contrib/sabcor/
    make

Update

EXAFS Neo is under active development, to update the code after pulling from the repository:

    git pull --rebase
    python setup.py install

Video Demonstrations

You can see a list of video demonstrations of the EXAFS Neo package presented, future presentation related to this software will be posted as they are available

• IIT EXAFS Workshop 2021 (Aug 23, 2021)
• University of Washington (Feb 15, 2021)
• Canadian Light Source (Dec 10, 2020)

Citation

Jeff Terry, Miu Lun Lau, Jiateng Sun, Chang Xu, Bryan Hendricks, Julia Kise, Mrinalini Lnu, Sanchayni Bagade, Shail Shah, Priyanka Makhijani, Adithya Karantha, Travis Boltz, Max Oellien, Matthew Adas, Shlomo Argamon, Min Long, and Donna Post Guillen, “Analysis of Extended X-ray Absorption Fine Structure (EXAFS) Data Using Artificial Intelligence Techniques,” Applied Surface Science 547, 149059 https://doi.org/10.1016/j.apsusc.2021.149059 (2021).

@article{terry2021analysis,
  title={Analysis of extended X-ray absorption fine structure (EXAFS) data using artificial intelligence techniques},
  author={Terry, Jeff and Lau, Miu Lun and Sun, Jiateng and Xu, Chang and Hendricks, Bryan and Kise, Julia and Lnu, Mrinalini and Bagade, Sanchayni and Shah, Shail and Makhijani, Priyanka and others},
  journal={Applied Surface Science},
  volume={547},
  pages={149059},
  year={2021},
  publisher={Elsevier}
}