easyXRD

We have developed a versatile X-ray diffraction (XRD) analysis tool that utilizes modern and open-source Python packages such as pyFAI, xarray, pymatgen, pybaselines,... for data processing/storage and interfaced to Jupyter notebooks powered with actively developed visualization packages such as ipywidgets, and matplotlib. It provides easy access to the Materials Project database which hosts thousands of crystal structures that can be used for phase identification - a critical part of XRD analysis - and utilizes GSAS-II suite for XRD refinements in a user-friendly and intuitive manner. Ultimate goal of this tool is to make X-ray diffraction analysis easy for users and help them to process, refine, store, and share their XRD data conveniently.

You can try the tutorial notebooks below on Google Colab:

01_basic.ipynb
02_intermediate.ipynb
03_advanced-part-1.ipynb
03_advanced-part-2.ipynb

.... more to come

In order to use easyXRD Python package, you need to have a Python environment that can be easily installed through Conda.

You can follow the instructions on this link: https://www.anaconda.com/docs/getting-started/miniconda/install

Examples of environment setup is shown below:

• Linux Bash:

mkdir -p ~/.miniconda3

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/.miniconda3/miniconda.sh
bash ~/.miniconda3/miniconda.sh -b -u -p ~/.miniconda3
rm ~/.miniconda3/miniconda.sh

source ~/.miniconda3/bin/activate
conda init --all

• Windows PowerShell (After installing, open the “Anaconda Powershell Prompt (miniconda3)”):

wget "https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe" -outfile ".\.miniconda.exe"
Start-Process -FilePath ".\.miniconda.exe" -ArgumentList "/S" -Wait
del .\.miniconda.exe

• macOS Bash (Apple Silicon):

mkdir -p ~/.miniconda3
curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh -o ~/.miniconda3/miniconda.sh
bash ~/.miniconda3/miniconda.sh -b -u -p ~/.miniconda3
rm ~/miniconda3/miniconda.sh

source ~/.miniconda3/bin/activate
conda init --all

• macOS Bash (Intel):

mkdir -p ~/.miniconda3
curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -o ~/.miniconda3/miniconda.sh
bash ~/.miniconda3/miniconda.sh -b -u -p ~/.miniconda3
rm ~/.miniconda3/miniconda.sh

source ~/.miniconda3/bin/activate
conda init --all

Once you have a working conda environment through the steps above, now we need to create a conda virtual environment that can be created as shown below:

conda create --name env_py3.11_np_1.26 -c conda-forge -y  python=3.11 numpy=1.26 jupyterlab

Once the new virtual environment is created, ne we need to activate it and then call jupyter lab interface

conda activate env_py3.11_np_1.26
cd
jupyter lab

Once you have the jupyter lab interface, we can continue from there.\

In a new cell, we need to install additional python packages for easyXRD.
You need to Copy-Paste the contents of code below into the Jupyter lab cell

# Here we install the necessary packages via pip
# It may take a while for this cell to complete. This will be quicker in next runs...
required_packages = {
    "mp-api",
    "scipy",
    "xarray",
    "h5netcdf",
    "ipympl",
    "pymatgen",
    "pyFAI==2024.9.0",
    "fabio==2024.9.0",
    "pybaselines",
    "ipympl",
    "easyxrd"
}
import subprocess,sys
for p in required_packages:
    subprocess.check_call([sys.executable, "-m", "pip", "install", p])
from IPython.display import clear_output
clear_output()

# Finally, we nedd to reset kernel for the package installations to take effect
import IPython
IPython.Application.instance().kernel.do_shutdown(True)

We can now import easyXRD. If you are running this cell for the first time, it will need to download GSAS-II libraries and binaries from GitHub. For the settings to have effect, the jupyter kernel will be restarted.

from easyxrd.core import exrd

After this step, you can contine to work with exrd as we explained in the Google Colab notebooks that are listed above.

It should be noted that, you need to acknowledge GSAS-II if you use the refinement components of easyXRD. You can check original GSAS-II repo, https://github.com/AdvancedPhotonSource/GSAS-II, for further details.

Feel free to contact me ([email protected]) if you have any questions about easyXRD