Metadata-Version: 2.1
Name: pygpcca
Version: 1.0.0
Summary: pyGPCCA - Generalized Perron Cluster Cluster Analysis
Home-page: https://github.com/msmdev/pygpcca
Author: Bernhard Reuter
Author-email: bernhard-reuter@gmx.de
Maintainer: Bernhard Reuter
Maintainer-email: bernhard-reuter@gmx.de
License: LGPLv3+
Download-URL: https://pypi.org/project/pygpcca/
Project-URL: Documentation, https://pygpcca.readthedocs.io/en/latest
Project-URL: Source Code, https://github.com/msmdev/pygpcca
Description: |PyPI| |Conda| |Cite| |CI| |Docs| |Coverage| |License| |PyPIdownloads|
        
        .. |PyPI| image:: https://img.shields.io/pypi/v/pygpcca
            :target: https://pypi.org/project/pygpcca/
            :alt: PyPI
        
        .. |Conda| image:: https://img.shields.io/conda/vn/conda-forge/pygpcca
            :target: https://anaconda.org/conda-forge/pygpcca
            :alt: Conda
        
        .. |Cite| image:: https://img.shields.io/badge/DOI-10.1021%2Facs.jctc.8b00079-blue
            :target: https://doi.org/10.1021/acs.jctc.8b00079
            :alt: Cite
        
        .. |CI| image:: https://img.shields.io/github/workflow/status/msmdev/pygpcca/CI/main
            :target: https://github.com/msmdev/pygpcca/actions
            :alt: CI
        
        .. |Docs|  image:: https://img.shields.io/readthedocs/pygpcca
            :target: https://pygpcca.readthedocs.io/en/latest
            :alt: Documentation
        
        .. |Coverage| image:: https://img.shields.io/codecov/c/github/msmdev/pygpcca/main
            :target: https://codecov.io/gh/msmdev/pygpcca
            :alt: Coverage
        
        .. |License| image:: https://img.shields.io/github/license/msmdev/pyGPCCA?color=green
            :target: https://github.com/msmdev/pyGPCCA/blob/main/LICENSE.txt
            :alt: License
        
        .. |PyPIdownloads| image:: https://static.pepy.tech/personalized-badge/requests?period=total&units=international_system&left_color=grey&right_color=blue&left_text=pypi%20downloads
            :target: https://pepy.tech/project/pygpcca
            :alt: PyPI - Downloads
        
        pyGPCCA - Generalized Perron Cluster Cluster Analysis
        =====================================================
        Generalized Perron Cluster Cluster Analysis program to coarse-grain reversible and non-reversible Markov State Models.
        
        Markov State Models (MSM) enable the identification and analysis of metastable states and related kinetics in a
        very instructive manner. They are widely used, e.g. to model molecular or cellular kinetics. \\
        Common state-of-the-art Markov state modeling methods and tools are very well suited to model reversible processes in
        closed equilibrium systems. However, most are not well suited to deal with non-reversible or even non-autonomous
        processes of non-equilibrium systems. \\
        To overcome this limitation, the Generalized Robust Perron Cluster Cluster Analysis (G-PCCA) was developed.
        The G-PCCA method implemented in the *pyGPCCA* program readily handles equilibrium as well as non-equilibrium data by
        utilizing real Schur vectors instead of eigenvectors. \\
        *pyGPCCA* enables the semiautomatic coarse-graining of transition matrices representing the dynamics of the system
        under study. Utilizing *pyGPCCA*, metastable states as well as cyclic kinetics can be identified and modeled.
        
        If you use *pyGPCCA* or parts of it, please cite `JCTC (2018)`_.
        
        .. _JCTC (2018): https://pubs.acs.org/doi/abs/10.1021/acs.jctc.8b00079
        
        Installation
        ------------
        We support multiple ways of installing *pyGPCCA*. If any problems arise, please consult the
        `troubleshooting <https://pygpcca.readthedocs.io/en/latest/installation.html#troubleshooting>`_
        section in the documentation.
        
        Conda
        +++++
        *pyGPCCA* is available as a `conda package <https://anaconda.org/conda-forge/pygpcca>`_ and can be installed as::
        
            conda install -c conda-forge pygpcca
        
        This is the recommended way of installing, since this package also includes `PETSc`_/`SLEPc`_ libraries.
        We use `PETSc`_/`SLEPc`_ internally to speed up the computation of leading Schur vectors (both are optional)
        
        .. _`PETSc`: https://www.mcs.anl.gov/petsc/
        
        PyPI
        ++++
        In order to install *pyGPCCA* from `The Python Package Index <https://pypi.org/project/pygpcca/>`_, run::
        
            pip install pygpcca
            # or with libraries utilizing PETSc/SLEPc
            pip install pygpcca[slepc]
        
        Example
        -------
        Please refer to our `example usage <https://pygpcca.readthedocs.io/en/latest/example.html>`_ in the documentation.
        
        Acknowledgements
        ----------------
        We thank `Marcus Weber`_ and the Computational Molecular Design (`CMD`_) group at the Zuse Institute Berlin (`ZIB`_)
        for the longstanding and productive collaboration in the field of Markov modeling of non-reversible molecular dynamics.
        M. Weber, together with K. Fackeldey, had the original idea to employ Schur vectors instead of eigenvectors in the
        coarse-graining of non-reversible transition matrices. \\
        Further, we would like to thank `Fabian Paul`_ for valuable discussions regarding the sorting of Schur vectors and his
        effort to translate the original Sorting routine for real Schur forms `SRSchur`_ published by `Jan Brandts`_ from MATLAB
        into `Python code`_,
        M. Weber and `Alexander Sikorski`_ for pointing us to `SLEPc`_ for sorted partial Schur decompositions,
        and A. Sikorski for supplying us with an `code example`_ and guidance how to interface SLEPc in Python.
        
        .. _`Marcus Weber`: https://www.zib.de/members/weber
        .. _`CMD`: https://www.zib.de/numeric/cmd
        .. _`ZIB`: https://www.zib.de/
        .. _`Fabian Paul`: https://github.com/fabian-paul
        .. _`SRSchur`: http://m2matlabdb.ma.tum.de/SRSchur.m?MP_ID=119
        .. _`Jan Brandts`: https://doi.org/10.1002/nla.274
        .. _`Python code`: https://gist.github.com/fabian-paul/14679b43ed27aa25fdb8a2e8f021bad5
        .. _`Alexander Sikorski`: https://www.zib.de/members/sikorski
        .. _`SLEPc`: https://slepc.upv.es/
        .. _`code example`: https://github.com/zib-cmd/cmdtools/blob/1c6b6d8e1c35bb487fcf247c5c1c622b4b665b0a/src/cmdtools/analysis/pcca.py#L64
Keywords: G-PCCA,GPCCA,Generalized Perron Cluster Cluster Analysis,Markov state model,Markov state modeling,Schur decomposition,Schur vectors,cellular dynamics,cellular kinetics,coarse-graining,cyclic states,metastable states,molecular dynamics,molecular kinetics,non-autonomous process,non-equilibrium system,non-reversible process,spectral clustering
Platform: Linux
Platform: MacOSX
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 or later (LGPLv3+)
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Typing :: Typed
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 :: Mathematics
Description-Content-Type: text/x-rst; charset=UTF-8
Provides-Extra: slepc
Provides-Extra: dev
Provides-Extra: test
Provides-Extra: docs
