Physical Review Materials· 2026Q1
diffpy.morph: Python tools for model-independent comparisons between sets of one-dimensional functions
- 1citations
- Q1SCImago
- 2026year
Short summary
The diffpy.morph Python package enables model-independent comparison of 1D scientific spectra by transforming datasets to remove unwanted experimental noise and benign physical changes, thereby highlighting nontrivial differences.
AI-generated from the title and abstract; the full text is not read.
Key points
- diffpy.morph is an open-source Python package for comparing 1D scientific spectra.
- It uses 'morphs' (transformations) to remove unwanted differences like experimental noise and thermal expansion effects.
- The package reveals nontrivial differences between datasets, aiding in the identification of chemical or structural changes.
- It is applicable to any 1D function, demonstrated here with x-ray and neutron diffraction/PDF data.
AI-generated from the title and abstract; the full text is not read.
Abstract
addresses a need to gain scientific insights from one-dimensional (1D) scientific spectra in model-independent ways. A powerful approach for this is to take differences between pairs of spectra and look for meaningful changes that might indicate underlying chemical, structural, or other modifications. The challenge is that the difference curve may contain uninteresting differences such as experimental inconsistencies and benign physical changes such as the effects of thermal expansion. allows researchers to apply simple transformations, or “morphs,” to one of the datasets to remove the unwanted differences, revealing, when they are present, nontrivial differences. is an open-source Python package available on the Python Package Index and conda-forge. Here, we describe its functionality and apply it to solve a range of experimental challenges on diffraction and pair distribution function data from x-rays and neutrons, though we note that it may be applied to any 1D function in principle.
The authors' abstract, as published at the source. Physical Review Materials, 2026 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Materials Chemistry
Materials ChemistryMaterials Science