Metadata-Version: 1.1
Name: pyEMMA
Version: 2.2.1
Summary: EMMA: Emma's Markov Model Algorithms
Home-page: http://github.com/markovmodel/PyEMMA
Author: Martin K. Scherer
Author-email: m.scherer@fu-berlin.de
License: LGPLv3+
Description: =====================================
        EMMA (Emma's Markov Model Algorithms)
        =====================================
        
        .. image:: https://travis-ci.org/markovmodel/PyEMMA.svg?branch=devel
           :target: https://travis-ci.org/markovmodel/PyEMMA
        .. image:: https://badge.fury.io/py/pyemma.svg
           :target: https://pypi.python.org/pypi/pyemma
        .. image:: https://img.shields.io/pypi/dm/pyemma.svg
           :target: https://pypi.python.org/pypi/pyemma
        .. image:: https://anaconda.org/xavier/binstar/badges/downloads.svg
           :target: https://anaconda.org/omnia/pyemma
        .. image:: https://anaconda.org/omnia/pyemma/badges/installer/conda.svg
           :target: https://conda.anaconda.org/omnia
        .. image:: https://coveralls.io/repos/markovmodel/PyEMMA/badge.svg?branch=devel
           :target: https://coveralls.io/r/markovmodel/PyEMMA?branch=devel
        
        
        What is it?
        -----------
        PyEMMA (EMMA = Emma's Markov Model Algorithms) is an open source
        Python/C package for analysis of extensive molecular dynamics simulations.
        In particular, it includes algorithms for estimation, validation and analysis
        of:
        
          * Clustering and Featurization
          * Markov state models (MSMs)
          * Hidden Markov models (HMMs)
          * Multi-ensemble Markov models (MEMMs)
          * Time-lagged independent component analysis (TICA)
          * Transition Path Theory (TPT)
        
        PyEMMA can be used from Jupyter (former IPython, recommended), or by
        writing Python scripts. The docs, can be found at
        `http://pyemma.org <http://www.pyemma.org/>`__.
        
        
        Citation
        --------
        If you use PyEMMA in scientific work, please cite:
        
            M. K. Scherer, B. Trendelkamp-Schroer, F. Paul, G. Pérez-Hernández,
            M. Hoffmann, N. Plattner, C. Wehmeyer, J.-H. Prinz and F. Noé:
            PyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Models,
            J. Chem. Theory Comput. 11, 5525-5542 (2015)
        
        
        Installation
        ------------
        With pip::
        
           pip install pyemma
        
        with conda::
        
           conda install -c omnia pyemma
        
        or install latest devel branch with pip::
        
           pip install git+https://github.com/markovmodel/PyEMMA.git@devel
        
        For a complete guide to installation, please have a look at the version 
        `online <http://www.emma-project.org/latest/INSTALL.html>`__ or offline in file
        doc/source/INSTALL.rst
        
        To build the documentation offline you should install the requirements with::
           
           pip install -r requirements-build-doc.txt
        
        Then build with make::
        
           cd doc; make html
        
        
        Support and development
        -----------------------
        For bug reports/suggestions/complaints please file an issue on 
        `GitHub <http://github.com/markovmodel/PyEMMA>`__.
        
        Or start a discussion on our mailing list: pyemma-users@lists.fu-berlin.de
        
        
        External Libraries
        ------------------
        * mdtraj (LGPLv3): https://mdtraj.org
        * bhmm (LGPLv3): http://github.com/bhmm/bhmm
        * msmtools (LGLPv3): http://github.com/markovmodel/msmtools
        * thermotools (LGLPv3): http://github.com/markovmodel/thermotools
        
Keywords: Markov State Model Algorithms
Platform: Windows
Platform: Linux
Platform: Solaris
Platform: Mac OS-X
Platform: Unix
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Environment :: MacOS X
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 or later (LGPLv3+)
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
Requires: numpy
Requires: scipy
