Welcome to Mrinversion documentation!¶
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About
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, \(\rho(\delta_\text{iso}, \zeta_\sigma, \eta_\sigma)\), where \(\delta_\text{iso}\) is the isotropic chemical shift, and \(\zeta_\sigma\) and \(\eta_\sigma\), are the shielding anisotropy and asymmetry parameters, respectively, 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, \(\rho(\delta_\text{iso}, \zeta_\sigma, \eta_\sigma)\), where \(\delta_\text{iso}\) is the isotropic chemical shift, and \(\zeta_\sigma\) and \(\eta_\sigma\), are the shielding anisotropy and asymmetry parameters, respectively, defined using the Haeberlen convention.
View our example gallery
Getting Started¶
Examples¶
Project 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). https://doi.org/10.1063/5.0023345.
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