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Welcome to Mrinversion documentation!
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      * - Deployment
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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 <https://mrsimulator.readthedocs.io/en/stable/>`_
package for generating solid-state NMR spectra and
`scikit-learn <https://scikit-learn.org/stable/>`_ package for statistical learning.

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**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, :math:`\rho(\delta_\text{iso}, \zeta_\sigma, \eta_\sigma)`,
  where :math:`\delta_\text{iso}` is the isotropic chemical shift, and :math:`\zeta_\sigma`
  and :math:`\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, :math:`\rho(\delta_\text{iso}, \zeta_\sigma, \eta_\sigma)`,
  where :math:`\delta_\text{iso}` is the isotropic chemical shift, and :math:`\zeta_\sigma`
  and :math:`\eta_\sigma`, are the shielding anisotropy and asymmetry parameters,
  respectively, defined using the Haeberlen convention.


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    **View our example gallery**

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Getting Started
---------------

.. toctree::
    :maxdepth: 2
    :caption: Getting Started

    installation
    requirement
    introduction
    before_getting_started
    getting_started
    referenceAPI

Examples
--------

.. toctree::
    :maxdepth: 1
    :caption: Examples

    auto_examples/index

Project details
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.. toctree::
    :maxdepth: 1
    :caption: Project details

    changelog
    credits/license
    credits/acknowledgment

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

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    Indices and tables
    ^^^^^^^^^^^^^^^^^^

    * :ref:`genindex`
    * :ref:`modindex`
    * :ref:`search`