Answers are grounded in this paper and cite their sources.
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.
No takes yet. Share an insight, caveat, or question.
Yang et al. (2026) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: