Welcome to Mrinversion documentation!¶
Deployment |
|
Build Status |
?logo=GitHub)
|
License |
|
Metrics |
|
GitHub |
|
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¶
Examples