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Improve RTD and README (#15)
* Ask to cite JCTC (2018) in index.rst. * Add License badge to index.rst. * Add Download badges to index.rst. * Add F. Paul to __credits__. * Add Acknowledgements to RTD. * Add Acknowledgements to README.rst. * Incorporate README.rst into index.rst. * Include Reference to SRSchur.
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‎README.rst‎

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|PyPI| |Conda| |Cite| |CI| |Docs| |Coverage|
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|PyPI| |Conda| |Cite| |CI| |Docs| |Coverage| |License| |PyPIdownloads|
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.. |PyPI| image:: https://img.shields.io/pypi/v/pygpcca
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:target: https://pypi.org/project/pygpcca/
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:alt: PyPI
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.. |Conda| image:: https://img.shields.io/conda/vn/conda-forge/pygpcca
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:target: https://anaconda.org/conda-forge/pygpcca
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:alt: Conda
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.. |Cite| image:: https://img.shields.io/badge/DOI-10.1021%2Facs.jctc.8b00079-blue
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:target: https://doi.org/10.1021/acs.jctc.8b00079
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:alt: Cite
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.. |CI| image:: https://img.shields.io/github/workflow/status/msmdev/pygpcca/CI/main
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:target: https://github.com/msmdev/pygpcca/actions
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:alt: CI
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.. |Docs| image:: https://img.shields.io/readthedocs/pygpcca
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:target: https://pygpcca.readthedocs.io/en/latest
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:alt: Documentation
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.. |Coverage| image:: https://img.shields.io/codecov/c/github/msmdev/pygpcca/main
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:target: https://codecov.io/gh/msmdev/pygpcca
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:alt: Coverage
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.. |License| image:: https://img.shields.io/github/license/msmdev/pyGPCCA?color=green
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:target: https://github.com/msmdev/pyGPCCA/blob/main/LICENSE.txt
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:alt: License
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.. |PyPIdownloads| image:: https://static.pepy.tech/personalized-badge/requests?period=total&units=international_system&left_color=grey&right_color=blue&left_text=pypi%20downloads
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:target: https://pepy.tech/project/requests
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:alt: PyPI - Downloads
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pyGPCCA - Generalized Perron Cluster Cluster Analysis
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=====================================================
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*pyGPCCA* enables the semiautomatic coarse-graining of transition matrices representing the dynamics of the system
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under study. Utilizing *pyGPCCA*, metastable states as well as cyclic kinetics can be identified and modeled.
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If you use *pyGPCCA* or parts of it, please cite `JCTC (2018)`_.
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.. _JCTC (2018): https://pubs.acs.org/doi/abs/10.1021/acs.jctc.8b00079
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Installation
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------------
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We support multiple ways of installing *pyGPCCA*. If any problems arise, please consult the
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This is the recommended way of installing, since this package also includes `PETSc`_/`SLEPc`_ libraries.
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We use `PETSc`_/`SLEPc`_ internally to speed up the computation of leading Schur vectors (both are optional)
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.. _`PETSc`: https://www.mcs.anl.gov/petsc/
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PyPI
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In order to install *pyGPCCA* from `The Python Package Index <https://pypi.org/project/pygpcca/>`_, run::
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-------
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Please refer to our `example usage <https://pygpcca.readthedocs.io/en/latest/example.html>`_ in the documentation.
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.. |PyPI| image:: https://img.shields.io/pypi/v/pygpcca
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:target: https://pypi.org/project/pygpcca/
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:alt: PyPI
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.. |Conda| image:: https://img.shields.io/conda/vn/conda-forge/pygpcca
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:target: https://anaconda.org/conda-forge/pygpcca
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:alt: Conda
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.. |Cite| image:: https://img.shields.io/badge/DOI-10.1021%2Facs.jctc.8b00079-blue
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:target: https://doi.org/10.1021/acs.jctc.8b00079
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:alt: Cite
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.. |CI| image:: https://img.shields.io/github/workflow/status/msmdev/pygpcca/CI/main
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:target: https://github.com/msmdev/pygpcca/actions
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:alt: CI
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.. |Docs| image:: https://img.shields.io/readthedocs/pygpcca
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:target: https://pygpcca.readthedocs.io/en/latest
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:alt: Documentation
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.. |Coverage| image:: https://img.shields.io/codecov/c/github/msmdev/pygpcca/main
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:target: https://codecov.io/gh/msmdev/pygpcca
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:alt: Coverage
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.. _`PETSc`: https://www.mcs.anl.gov/petsc/
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Acknowledgements
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----------------
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We thank `Marcus Weber`_ and the Computational Molecular Design (`CMD`_) group at the Zuse Institute Berlin (`ZIB`_)
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for the longstanding and productive collaboration in the field of Markov modeling of non-reversible molecular dynamics.
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M. Weber, together with K. Fackeldey, had the original idea to employ Schur vectors instead of eigenvectors in the
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coarse-graining of non-reversible transition matrices. |br|
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Further, we would like to thank `Fabian Paul`_ for valuable discussions regarding the sorting of Schur vectors and his
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effort to translate the original Sorting routine for real Schur forms `SRSchur`_ published by `Jan Brandts`_ from MATLAB
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into `Python code`_,
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M. Weber and `Alexander Sikorski`_ for pointing us to `SLEPc`_ for sorted partial Schur decompositions,
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and A. Sikorski for supplying us with an `code example`_ and guidance how to interface SLEPc in Python.
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.. _`Marcus Weber`: https://www.zib.de/members/weber
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.. _`CMD`: https://www.zib.de/numeric/cmd
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.. _`ZIB`: https://www.zib.de/
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.. _`Fabian Paul`: https://github.com/fabian-paul
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.. _`SRSchur`: http://m2matlabdb.ma.tum.de/SRSchur.m?MP_ID=119
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.. _`Jan Brandts`: https://doi.org/10.1002/nla.274
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.. _`Python code`: https://gist.github.com/fabian-paul/14679b43ed27aa25fdb8a2e8f021bad5
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.. _`Alexander Sikorski`: https://www.zib.de/members/sikorski
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.. _`SLEPc`: https://slepc.upv.es/
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.. _`code example`: https://github.com/zib-cmd/cmdtools/blob/1c6b6d8e1c35bb487fcf247c5c1c622b4b665b0a/src/cmdtools/analysis/pcca.py#L64
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.. |br| raw:: html
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‎docs/source/acknowledgements.rst‎

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Acknowledgements
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================
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We thank `Marcus Weber`_ and the Computational Molecular Design (`CMD`_) group at the Zuse Institute Berlin (`ZIB`_)
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for the longstanding and productive collaboration in the field of Markov modeling of non-reversible molecular dynamics.
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M. Weber, together with K. Fackeldey, had the original idea to employ Schur vectors instead of eigenvectors in the
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coarse-graining of non-reversible transition matrices.
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Further, we would like to thank `Fabian Paul`_ for valuable discussions regarding the sorting of Schur vectors and his
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effort to translate the original Sorting routine for real Schur forms `SRSchur`_ published by `Jan Brandts`_ from MATLAB
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into `Python code`_,
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M. Weber and `Alexander Sikorski`_ for pointing us to `SLEPc`_ for sorted partial Schur decompositions,
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and A. Sikorski for supplying us with an `code example`_ and guidance how to interface SLEPc in Python.
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.. _`Marcus Weber`: https://www.zib.de/members/weber
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.. _`CMD`: https://www.zib.de/numeric/cmd
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.. _`ZIB`: https://www.zib.de/
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.. _`Fabian Paul`: https://github.com/fabian-paul
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.. _`SRSchur`: http://m2matlabdb.ma.tum.de/SRSchur.m?MP_ID=119
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.. _`Jan Brandts`: https://doi.org/10.1002/nla.274
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.. _`Python code`: https://gist.github.com/fabian-paul/14679b43ed27aa25fdb8a2e8f021bad5
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.. _`Alexander Sikorski`: https://www.zib.de/members/sikorski
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.. _`SLEPc`: https://slepc.upv.es/
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.. _`code example`: https://github.com/zib-cmd/cmdtools/blob/1c6b6d8e1c35bb487fcf247c5c1c622b4b665b0a/src/cmdtools/analysis/pcca.py#L64

‎docs/source/index.rst‎

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|PyPI| |Conda| |Cite| |CI| |Docs| |Coverage|
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pyGPCCA - Generalized Perron Cluster Cluster Analysis
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=====================================================
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Generalized Perron Cluster Cluster Analysis program to coarse-grain reversible and non-reversible Markov State Models.
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Markov State Models (MSM) enable the identification and analysis of metastable states and related kinetics in a
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very instructive manner. They are widely used, e.g. to model molecular or cellular kinetics. |br|
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Common state-of-the-art Markov state modeling methods and tools are very well suited to model reversible processes in
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closed equilibrium systems. However, most are not well suited to deal with non-reversible or even non-autonomous
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processes of non-equilibrium systems. |br|
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To overcome this limitation, the Generalized Robust Perron Cluster Cluster Analysis (G-PCCA) was developed.
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The G-PCCA method implemented in the *pyGPCCA* program readily handles equilibrium as well as non-equilibrium data by
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utilizing real Schur vectors instead of eigenvectors. |br|
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*pyGPCCA* enables the semiautomatic coarse-graining of transition matrices representing the dynamics of the system
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under study. Utilizing *pyGPCCA*, metastable states as well as cyclic kinetics can be identified and modeled.
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.. include:: ../../README.rst
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:end-line: 60
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.. include:: _key_contributors.rst
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installation
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api
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example
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acknowledgements
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references
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<br/>

‎pygpcca/_gpcca.py‎

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"Susanna Roeblitz",
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"Marius Lange",
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"Michal Klein",
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"Fabian Paul",
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"Alexander Sikorski",
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]
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