High-performance Python for crystallographic computing

Abstract

The Python programming language, combined with the numerical computing library NumPy and the scientific computing library SciPy, has become the de facto standard for scientific computing in a variety of fields. This popularity is mainly due to the ease with which a Python program can be written and executed (easy syntax, dynamical typing, no compilation etc.), coupled with the existence of a large number of specialized third-party libraries that aim to lift all the limitations of the raw Python language. NumPy introduces vector programming, improving execution speeds, whereas SciPy brings a wealth of highly optimized and reliable scientific functions. There are cases, however, where vector programming alone is not sufficient to reach optimal performance. This issue is addressed with dedicated compilers that aim to translate Python code into native and statically typed code with support for the multi-core architectures of modern processors. In the present article it is shown how these approaches can be efficiently used to tackle different problems, with increasing complexity, that are relevant to crystallography: the 2D Laue function, scattering from a strained 2D crystal, scattering from 3D nanocrystals and, finally, diffraction from films and multilayers. For each case, detailed implementations and explanations of the functioning of the algorithms are provided. Different Python compilers (namely NumExpr, Numba, Pythran and Cython) are used to improve performance and are benchmarked against stateof-the-art NumPy implementations. All examples are also provided as commented and didactic Python (Jupyter) notebooks that can be used as starting points for crystallographers curious to enter the Python ecosystem or wishing to accelerate their existing codes. ISSN 1600-5767 teaching and education J. Appl. Cryst. (2019). 52, 882-897 Boulle and Kieffer High-performance Python for crystallographic computing 883 Figure 1Fraction of articles published every year by the International Union of Crystallography containing the word 'Python' in the title or in the abstract.

A practical, effective calculation of gamma difference distributions with open data science tools

“…It is also worth to mention that the speed of our tool is not final. We assume that parallelization will lead to at least one order faster code [54] than now. This is our current work in progress since the need to keep track of internal states in some loops of the DE quadrature makes it difficult to find an optimal parallelization approach.…”

“…Appendix C. Open digital tools in the numerical study In applying these compilers, we were inspired by [54].…”

Gwaihir: Jupyter Notebook graphical user interface for Bragg coherent diffraction imaging

“…A part of the BCDI community relies on Python, an accessible language that has gradually become one of the most popular, versatile (Perez & Granger, 2007;Newville et al, 2016) and widely taught (Scopatz & Huff, 2015;McKinney, 2017;Boulle & Kieffer, 2019) programming languages in science. Gwaihir followed this initiative by regrouping data visualization tools, workflow guidelines and a user-friendly interface around two Python packages (PyNX and bcdi) that, together, offer a complete data analysis suite (see Figs.…”

RaDMaX online: a web-based program for the determination of strain and damage profiles in irradiated crystals using X-ray diffraction

“…operations implicitly working on all components of a vector (Oliphant, 2007;van der Walt et al, 2011), and the SciPy library (https://www.scipy.org/) which provides a wealth of scientific functions. Further details regarding crystallographic computing using the Python programming language can be found in (Boulle & Kieffer, 2019). The structure factor of the material is computed using the xrayutilities python package (Kriegner et al, 2013) which, among other things, allows to read crystallographic information files (cif) which contain the description of the structure of the crystal (Hall et al, 1991).…”