Metadata-Version: 2.1
Name: mrinversion
Version: 0.2.0
Summary: Python based statistical learning of NMR tensor parameters distribution from 2D isotropic/anisotropic NMR correlation spectra.
Home-page: https://github.com/DeepanshS/mrinversion/
Author: Deepansh J. Srivastava
Author-email: deepansh2012@gmail.com
License: BSD-3-Clause
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: matplotlib
License-File: LICENSE

# Mrinversion

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| ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Deployment   | [![PyPI version](https://img.shields.io/pypi/v/mrinversion.svg?style=flat&logo=pypi&logoColor=white)](https://pypi.python.org/pypi/mrinversion) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mrinversion)                                                                                                                                                                                                                                              |
| Build Status | [![GitHub Workflow Status](<https://img.shields.io/github/workflow/status/deepanshs/mrinversion/CI%20(pip)?logo=GitHub>)](https://github.com/DeepanshS/mrinversion/actions) [![Read the Docs](https://img.shields.io/readthedocs/mrinversion)](https://mrinversion.readthedocs.io/en/latest/)                                                                                                                                                                             |
| License      | [![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)                                                                                                                                                                                                                                                                                                                                                 |
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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](https://mrsimulator.readthedocs.io/en/latest/)
package for generating solid-state NMR spectra and
[scikit-learn](https://scikit-learn.org/latest/) 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](https://mrinversion.readthedocs.io/en/latest/).

> **View our example gallery**
>
> [![](https://img.shields.io/badge/View-Example%20Gallery-Purple?s=small)](https://mrinversion.readthedocs.io/en/latest/auto_examples/index.html)

## Installation

    $ pip install mrinversion

Please read our [installation document](https://mrinversion.readthedocs.io/en/latest/installation.html) 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](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


