diffpy.morph/CITATION.cff at main · diffpy/diffpy.morph · GitHub

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cff-version: 1.2.0
title: diffpy.morph
message: >-
  If you use this software, please cite the manuscript
  associated with this repository (will be included here
  when published).
type: software
authors:
  - given-names: Andrew
    family-names: Yang
    email: ayang2@caltech.edu
    affiliation: Caltech
    orcid: "https://orcid.org/0000-0003-0553-715X"
  - given-names: Christopher
    family-names: Farrow
    orcid: "https://orcid.org/0000-0001-5768-6654"
  - given-names: Chia-Hao
    family-names: Liu
    affiliation: Columbia University
    orcid: "https://orcid.org/0000-0002-3216-0354"
  - given-names: Luis
    family-names: Kitsu
    orcid: "https://orcid.org/0000-0002-9292-4416"
    affiliation: University of Colorado Boulder
    email: Luis.Kitsu@echemes.ethz.ch
  - given-names: Simon
    family-names: Billinge
    email: sb2896@columbia.edu
    affiliation: Columbia University
    orcid: "https://orcid.org/0000-0002-9734-4998"
abstract: >-
  diffpy.morph is an open-source and free-to-use Python
  package that increases the insight researchers can obtain
  when comparing experimentally measured 1D functions such
  as diffraction patterns and atomic distribution functions
  (PDFs). For example, it is often difficult to identify
  whether or not structural changes have occurred between
  two diffraction patterns or PDFs of the same material
  measured at different temperatures due to the presence of
  benign thermal effects such as lattice expansion and
  increased atomic motion. These contribute large signals in
  the difference curves and Rw factors when comparing two
  curves, which can hide small but significant structural
  changes. diffpy.morph does its best to correct for these
  benign effects by applying simple transformations, or
  “morphs”, to a diffraction pattern or PDF prior to
  comparison. Other morphs are also possible such as
  corrections for nanoparticle shape effects on the PDF.
  diffpy.morph is model-independent and also could be used
  on other non-diffraction spectra, though it has not been
  extensively tested beyond powder diffraction patterns
  and the PDF.
keywords:
  - diffpy
  - pdf
  - data interpretation
license: BSD-3-Clause