Mrinversion
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The mrinversion python package is based on the statistical learning technique for
determining the distribution of the magnetic resonance (NMR) tensor parameters
from two-dimensional NMR spectra correlating the isotropic to anisotropic
frequencies.
The library utilizes the mrsimulator
package for generating solid-state NMR spectra and
scikit-learn package for statistical learning.
Features
The mrinversion package includes the inversion of a two-dimensional
solid-state NMR spectrum of dilute spin-systems to a three-dimensional distribution of
tensor parameters. At present, we support the inversion of
-
Magic angle turning (MAT), Phase adjusted spinning sidebands (PASS), and similar spectra correlating the isotropic chemical shift resonances to pure anisotropic spinning sideband resonances into a three-dimensional distribution of nuclear shielding tensor parameters---isotropic chemical shift, shielding anisotropy and asymmetry parameters---defined using the Haeberlen convention.
-
Magic angle flipping (MAF) spectra correlating the isotropic chemical shift resonances to pure anisotropic resonances into a three-dimensional distribution of nuclear shielding tensor parameters---isotropic chemical shift, shielding anisotropy and asymmetry parameters---defined using the Haeberlen convention.
For more information, refer to the documentation.
View our example gallery
Installation
$ pip install mrinversion
Please read our installation document for details.
How to cite
If you use this work in your publication, please cite the following.
-
Srivastava, D. J.; Grandinetti P. J., Statistical learning of NMR tensors from 2D isotropic/anisotropic correlation nuclear magnetic resonance spectra, J. Chem. Phys. 153, 134201 (2020). DOI:10.1063/5.0023345.
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Deepansh J. Srivastava, Maxwell Venetos, Philip J. Grandinetti, Shyam Dwaraknath, & Alexis McCarthy. (2021, May 26). mrsimulator: v0.6.0 (Version v0.6.0). Zenodo. http://doi.org/10.5281/zenodo.4814638
Additionally, if you use the CSDM data model, please consider citing
- Srivastava DJ, Vosegaard T, Massiot D, Grandinetti PJ (2020) Core Scientific Dataset Model: A lightweight and portable model and file format for multi-dimensional scientific data. PLOS ONE 15(1): e0225953. https://doi.org/10.1371/journal.pone.0225953
