A Jupyter notebook for unit cell fitting
Unit-cell parameter fitting with statistical indices for outlier points.
This notebook shows how to conduct unit-cell fitting using the strategy presented in Holland and Redfurn (1997) using the statsmodels and lmfit packages.
You need Miller index and two theta angle for the input.
Often unit cell fitting is an iterative process. Peak positions obtained from peak fitting can be biased by a range of issues, including peak overlap and weak intensity. Therefore, it is important to know which peaks are outtliers and which peaks are not.
Holland and Redfern proposed a robust statistical approach for this problem (read below).
T. J. B. Holland and S. A. T. Redfern (1997) "Unit cell refinement from powder diffraction data: the use of regression diagnostics". Mineralogical Magazine 61: 65-77.
Based on this approach, Holland and Redfern made a software package, UnitCell (see URL below). However, the software is outdated for the recent MacOS. Therefore, this notebook can provide a good alternative.
Major functions include:
- Unit-cell fitting
- Outlier statistics
(Note) 2022/03/04 Monoclinic and triclinic cells are not supported
S.-H. Shim (2022) Unit cell fitting in Jupyter. Zenodo. http://doi.org/10.5281/zenodo.6344949